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| text      = This is sublevel4 of my sandbox, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see [[User_talk:Shawndouglas|my discussion page]] instead.<p></p>
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==Overview of the cannabis industry in the United States==
==Sandbox begins below==
The following is a brief overview of the cannabis industry in the United States. It's meant to give a quick and concise review of where cannabis use, regulation, testing, and research have been and where they are now. Many of the topics touched upon here will be expanded upon later in this guide.


===Brief history of cannabis in the U.S.===
*Discussion and practical use of [[artificial intelligence]] (AI) in the [[laboratory]] is, perhaps to the surprise of some, not a recent phenomena. In the mid-1980s, researchers were developing computerized AI systems able "to develop automatic decision rules for follow-up analysis of &#91;[[clinical laboratory]]&#93; tests depending on prior information, thus avoiding the delays of traditional sequential testing and the costs of unnecessary parallel testing."<ref>{{Cite journal |last=Berger-Hershkowitz |first=H. |last2=Neuhauser |first2=D. |date=1987 |title=Artificial intelligence in the clinical laboratory |url=https://www.ccjm.org/content/54/3/165 |journal=Cleveland Clinic Journal of Medicine |volume=54 |issue=3 |pages=165–166 |doi=10.3949/ccjm.54.3.165 |issn=0891-1150 |pmid=3301059}}</ref> In fact, discussion of AI in general was ongoing even in the mid-1950s.<ref name="MinskyHeuristic56">{{cite book |url=https://books.google.com/books?hl=en&lr=&id=fvWNo6_IZGUC&oi=fnd&pg=PA1 |title=Heuristic Aspects of the Artificial Intelligence Problem |author=Minsky, M. |publisher=Ed Services Technical Information Agency |date=17 December 1956 |accessdate=16 February 2023}}</ref><ref>{{Cite journal |last=Minsky |first=Marvin |date=1961-01 |title=Steps toward Artificial Intelligence |url=http://ieeexplore.ieee.org/document/4066245/ |journal=Proceedings of the IRE |volume=49 |issue=1 |pages=8–30 |doi=10.1109/JRPROC.1961.287775 |issn=0096-8390}}</ref>
''Cannabis'' is a rapid-growing, flowering plant that has been used for centuries for industrial, medicinal, and recreational purposes. The plant includes three species or subspecies: ''indica'', ''ruderalis'', and ''sativa''.<ref name="GRINCannabis11">{{cite web |url=https://npgsweb.ars-grin.gov/gringlobal/taxonomygenus.aspx?id=2034 |title=Genus: Cannabis L. |work=U.S. National Plant Germplasm System |publisher=U.S. Department of Agriculture |date=01 January 2011 |accessdate=20 January 2017}}</ref> Both industrial hemp and recreational marijuana are derived from cannabis plants, but with important differences in biochemical composition. Hemp — which has historically been used to create clothing, food and feed, paper, textiles, and other industrial items — tends to have lower levels of the psychoactive component tetrahydrocannabinol (THC) and higher levels of the non-psychoactive component cannabidiol (CBD).<ref name="SwansonControlled15">{{cite journal |title=Controlled Substances Chaos: The Department of Justice's New Policy Position on Marijuana and What It Means for Industrial Hemp Farming in North Dakota |journal=North Dakota Law Review |author=Swanson, T.E. |volume=90 |issue=3 |pages=599–622 |year=2015 |url=https://law.und.edu/_files/docs/ndlr/pdf/issues/90/3/90ndlr599.pdf |format=PDF}}</ref><ref name="DeitchHemp03">{{cite book |title=Hemp – American History Revisited |author=Deitch, R. |publisher=Algora Publishing |location=New York City |year=2003 |pages=232 |isbn=9780875862262}}</ref> Some cannabis strains have intentionally been bred to produce low levels of THC, while others have been bred with the intent to maximize the psychoactive component.


Cannabis cultivation began in England's Jamestown colony of America in earnest around 1611, via formal orders. Several years later those orders turned into a royal decree, enacted by the Virginia Company, asking colonists to grow 100 hemp plants for export to England.<ref name="DeitchHemp03" /> Colonial American continued its growth, use, and exportation of hemp, even beyond the foundation of the United States. During that time, growers undoubtedly were using the female plant (which flowers and has higher levels of THC) to treat aches and pains as well as enjoy it recreationally. By the time of the U.S. Civil War arrived in the 1860s, however,the growth and use of industrial hemp declined as increased cotton and wood use took away much of the profitability of hemp.<ref name="DeitchHemp03" /> Around the same time, local governments began recognizing tonics, tinctures, and extracts from cannabis plants as potentially dangerous substances, labeling them as hypnotics, narcotics, or even poisons.<ref name="Senate1860">{{cite web |url=http://www.nytimes.com/1860/02/16/news/senate-88150825.html |title=Senate |author=U.S. Senate |work=The New York Times |date=15 February 1860 |accessdate=20 January 2017}}</ref> In the early twentieth century, U.S. labeling and prescription laws — such as the the Pure Food and Drug Act of 1906 at the federal level as well as various state laws — saw further restrictions put on cannabis, effectively culminating in the Marihuana Tax Act of 1937 and the Federal Food, Drug, and Cosmetic Act of 1938. With the passage of those acts, hemp and marijuana essentially became illegal, controlled substances.<ref name="WaltonMari38">{{cite book |author=Walton, R.F. |title=Marijuana, America’s New Drug Problem |location=Philadelphia |publisher=B. Lippincott |year=1938 |page=37}}</ref><ref name="WoodwardTax37">{{cite web |url=http://www.druglibrary.org/schaffer/hemp/taxact/woodward.htm |title=Taxation of Marihuana |author=Woodward, W.C.; House of Representatives, Committee on Ways and Means |work=Schaffer Library of Drug Policy |date=04 May 1937 |accessdate=20 January 2017}}</ref><ref name="CaversTheFood39">{{cite journal |title=The Food, Drug, and Cosmetic Act of 1938: Its Legislative History and its Substantive Provisions |journal=Law and Contemporary Problems |author=Cavers, D.F. |volume=6 |pages=2–42 |year=1939 |url=http://scholarship.law.duke.edu/lcp/vol6/iss1/2/}}</ref>
*Hiring demand for laboratorians with AI experience (2015–18) has historically been higher in non-healthcare industries, such as manufacturing, mining, and agriculture, shedding a light on how AI adoption in the clinical setting may be lacking. According to the Brookings Institute, "Even for the relatively-skilled job postings in hospitals, which includes doctors, nurses, medical technicians, research lab workers, and managers, only approximately 1 in 1,250 job postings required AI skills." They add: "AI adoption may be slow because it is not yet useful, or because it may not end up being as useful as we hope. While our view is that AI has great potential in health care, it is still an open question."<ref name=":11">{{Cite web |last=Goldfarb, A.; Teodoridis, F. |date=09 March 2022 |title=Why is AI adoption in health care lagging? |work=Series: The Economics and Regulation of Artificial Intelligence and Emerging Technologies |url=https://www.brookings.edu/research/why-is-ai-adoption-in-health-care-lagging/ |publisher=Brookings Institute |accessdate=17 February 2023}}</ref>


State efforts to decriminalize marijuana were somewhat successful in the early 1970s, though progress towards that goal slowed again with the Reagan Administration's war on drugs.<ref name="MeierPolitics16">{{cite book |url=https://books.google.com/books?id=J4wYDQAAQBAJ&pg=PT58 |title=The Politics of Sin: Drugs, Alcohol and Public Policy: Drugs, Alcohol and Public Policy |author=Meier, K.J. |publisher=Taylor & Francis |year=2016 |page=58 |isbn=9781315287270}}</ref> Progress picked up steam again in the late 1990s into the 2000s, particularly in states such as California, Massachusetts, Connecticut, Washington, and Colorado.
*Today, AI is being practically used in not only clinical diagnostic laboratories but also clinical research labs, life science labs, and research and development (R&D) labs, and more. Practical uses of AI can be found in:


As of January 2017, twenty-eight U.S. states have approved some sort of broad decriminalization or legalization of medicinal and/or recreational marijuana.<ref name="SteinmetzThese16">{{cite web |url=http://time.com/4559278/marijuana-election-results-2016/ |title=These States Just Legalized Marijuana |author=Steinmetz, K. |work=Time |publisher=Time, Inc |date=08 November 2016 |accessdate=20 January 2017}}</ref> Industrial hemp has also been addressed in some regard, with 16 states having legalized commercialized industrial help production and the federal government making certain concessions on it (''Cannabis sativa'' containing no more than 0.3 percent THC, grown under a state-sanctioned agricultural pilot program).<ref name="NCSLState16">{{cite web |url=http://www.ncsl.org/research/agriculture-and-rural-development/state-industrial-hemp-statutes.aspx |title=State Industrial Hemp Statuses |publisher=National Conference of State Legislatures |date=19 August 2016 |accessdate=20 January 2017}}</ref><ref name="81FR53395">{{cite journal |url=https://www.federalregister.gov/documents/2016/08/12/2016-19146/statement-of-principles-on-industrial-hemp |journal=Federal Register |title=Statement of Principles on Industrial Hemp |volume=81 |issue=156 |date=12 August 2016 |pages=53395–6 |accessdate=14 February 2017}}</ref> However, cannabis, including industrial hemp, remains a Schedule I controlled substance, as determined by the U.S. Food and Drug Administration<ref name="LegerMari16">{{cite web |url=http://www.usatoday.com/story/news/2016/08/11/dea-marijuana-remains-illegal-under-federal-law/88550804/ |title=Marijuana to remain illegal under federal law, DEA says |author=Leger, D.L. |work=USA. Today |publisher=Gannett Company |date=11 August 2016 |accessdate=20 January 2017}}</ref>, including extracts and other derivatives such as cannabidiol (CBD).<ref name="WallaceLegal17">{{cite web |url=http://www.thecannabist.co/2017/01/13/hemp-dea-extracts-marijuana-cbd-judicial-review/71387/ |title=Legal challenge filed against DEA’s new marijuana extract rule |work=The Cannabist |author=Wallace, A. |publisher=The Denver Post |date=13 January 2017 |accessdate=14 February 2017}}</ref> This federal classification continues to clash with changing state laws and regulations at an increasing pace, creating both opportunities and difficulties for involved citizens at all points along the industrial, economic, and social chain.
:clinical research labs<ref name=":0">{{Cite journal |last=Damiani |first=A. |last2=Masciocchi |first2=C. |last3=Lenkowicz |first3=J. |last4=Capocchiano |first4=N. D. |last5=Boldrini |first5=L. |last6=Tagliaferri |first6=L. |last7=Cesario |first7=A. |last8=Sergi |first8=P. |last9=Marchetti |first9=A. |last10=Luraschi |first10=A. |last11=Patarnello |first11=S. |date=2021-12-07 |title=Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator |url=https://www.frontiersin.org/articles/10.3389/fcomp.2021.768266/full |journal=Frontiers in Computer Science |volume=3 |pages=768266 |doi=10.3389/fcomp.2021.768266 |issn=2624-9898}}</ref>
:hospitals<ref name=":0" /><ref name=":1">{{Cite journal |last=University of California, San Francisco |last2=Adler-Milstein |first2=Julia |last3=Aggarwal |first3=Nakul |last4=University of Wisconsin-Madison |last5=Ahmed |first5=Mahnoor |last6=National Academy of Medicine |last7=Castner |first7=Jessica |last8=Castner Incorporated |last9=Evans |first9=Barbara J. |last10=University of Florida |last11=Gonzalez |first11=Andrew A. |date=2022-09-29 |title=Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis |url=https://nam.edu/meeting-the-moment-addressing-barriers-and-facilitating-clinical-adoption-of-artificial-intelligence-in-medical-diagnosis |journal=NAM Perspectives |volume=22 |issue=9 |doi=10.31478/202209c |pmc=PMC9875857 |pmid=36713769}}</ref>
:medical diagnostics labs<ref name=":1" /><ref name=":12">{{Cite web |last=Government Accountability Office (GAO); National Academy of Medicine (NAM) |date=September 2022 |title=Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics |url=https://www.gao.gov/assets/gao-22-104629.pdf |format=PDF |publisher=Government Accountability Office |accessdate=16 February 2023}}</ref><ref name=":13">{{Cite journal |last=Wen |first=Xiaoxia |last2=Leng |first2=Ping |last3=Wang |first3=Jiasi |last4=Yang |first4=Guishu |last5=Zu |first5=Ruiling |last6=Jia |first6=Xiaojiong |last7=Zhang |first7=Kaijiong |last8=Mengesha |first8=Birga Anteneh |last9=Huang |first9=Jian |last10=Wang |first10=Dongsheng |last11=Luo |first11=Huaichao |date=2022-09-24 |title=Clinlabomics: leveraging clinical laboratory data by data mining strategies |url=https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04926-1 |journal=BMC Bioinformatics |language=en |volume=23 |issue=1 |pages=387 |doi=10.1186/s12859-022-04926-1 |issn=1471-2105 |pmc=PMC9509545 |pmid=36153474}}</ref><ref name=":7">{{Cite journal |last=DeYoung |first=B. |last2=Morales |first2=M. |last3=Giglio |first3=S. |date=2022-08-04 |title=Microbiology 2.0–A “behind the scenes” consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories |url=https://www.frontiersin.org/articles/10.3389/fmicb.2022.976068/full |journal=Frontiers in Microbiology |volume=13 |pages=976068 |doi=10.3389/fmicb.2022.976068 |issn=1664-302X |pmc=PMC9386241 |pmid=35992715}}</ref><ref name=":5">{{Cite web |last=Schut, M. |date=01 December 2022 |title=Get better with bytes |url=https://www.amsterdamumc.org/en/research/news/get-better-with-bytes.htm |publisher=Amsterdam UMC |accessdate=16 February 2023}}</ref><ref name="AlbanoCal19">{{cite web |url=https://physicianslab.com/calculations-to-diagnosis-the-artificial-intelligence-shift-thats-already-happening/ |title=Calculations to Diagnosis: The Artificial Intelligence Shift That’s Already Happening |author=Albano, V.; Morris, C.; Kent, T. |work=Physicians Lab |date=06 December 2019 |accessdate=16 February 2023}}</ref>
:chromatography labs<ref name="AlbanoCal19" />
:biology and life science labs<ref name=":6">{{Cite journal |last=de Ridder |first=Dick |date=2019-01 |title=Artificial intelligence in the lab: ask not what your computer can do for you |url=https://onlinelibrary.wiley.com/doi/10.1111/1751-7915.13317 |journal=Microbial Biotechnology |language=en |volume=12 |issue=1 |pages=38–40 |doi=10.1111/1751-7915.13317 |pmc=PMC6302702 |pmid=30246499}}</ref>
:medical imaging centers<ref name="Brandao-de-ResendeAIWeb22">{{cite web |url=https://siim.org/page/22w_clinical_adoption_of_ai |title=AI Webinar: Clinical Adoption of AI Across Image Producing Specialties |author=Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. |publisher=Society for Imaging Informatics in Medicine |date=11 October 2022}}</ref>
:ophthalmology clinics<ref>{{Cite journal |last=He |first=Mingguang |last2=Li |first2=Zhixi |last3=Liu |first3=Chi |last4=Shi |first4=Danli |last5=Tan |first5=Zachary |date=2020-07 |title=Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge |url=https://journals.lww.com/10.1097/APO.0000000000000301 |journal=Asia-Pacific Journal of Ophthalmology |language=en |volume=9 |issue=4 |pages=299–307 |doi=10.1097/APO.0000000000000301 |issn=2162-0989}}</ref>
:reproduction clinics<ref name=":9">{{Cite journal |last=Trolice |first=Mark P. |last2=Curchoe |first2=Carol |last3=Quaas |first3=Alexander M |date=2021-07 |title=Artificial intelligence—the future is now |url=https://link.springer.com/10.1007/s10815-021-02272-4 |journal=Journal of Assisted Reproduction and Genetics |language=en |volume=38 |issue=7 |pages=1607–1612 |doi=10.1007/s10815-021-02272-4 |issn=1058-0468 |pmc=PMC8260235 |pmid=34231110}}</ref><ref name="ESHREArti22">{{cite web |url=https://www.focusonreproduction.eu/article/ESHRE-News-22AI |title=Annual Meeting 2022: Artificial intelligence in embryology and ART |author=European Society of Human Reproduction and Embryology |work=Focus on Reproduction |date=06 July 2022 |accessdate=16 February 2023}}</ref><ref name="HinckleyApply21">{{cite web |url=https://rscbayarea.com/blog/applying-ai-for-better-ivf-success |title=Applying AI (Artificial Intelligence) in the Lab for Better IVF Success |author=Hinckley, M. |work=Reproductive Science Center Blog |publisher=Reproductive Science Center of the Bay Area |date=17 March 2021 |accessdate=16 February 2023}}</ref>
:digital pathology labs<ref name="YousifArt21">{{cite web |url=https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/ |title=Artificial intelligence is the key driver for digital pathology adoption |author=Yousif, M.; McClintock, D.S.; Yao, K. |work=Clinical Laboratory Int |publisher=PanGlobal Media |date=2021 |accessdate=16 February 2023}}</ref>
:material testing labs<ref name=":2">{{Cite journal |last=MacLeod |first=B. P. |last2=Parlane |first2=F. G. L. |last3=Morrissey |first3=T. D. |last4=Häse |first4=F. |last5=Roch |first5=L. M. |last6=Dettelbach |first6=K. E. |last7=Moreira |first7=R. |last8=Yunker |first8=L. P. E. |last9=Rooney |first9=M. B. |last10=Deeth |first10=J. R. |last11=Lai |first11=V. |date=2020-05-15 |title=Self-driving laboratory for accelerated discovery of thin-film materials |url=https://www.science.org/doi/10.1126/sciadv.aaz8867 |journal=Science Advances |language=en |volume=6 |issue=20 |pages=eaaz8867 |doi=10.1126/sciadv.aaz8867 |issn=2375-2548 |pmc=PMC7220369 |pmid=32426501}}</ref><ref name=":3">{{Cite journal |last=Chibani |first=Siwar |last2=Coudert |first2=François-Xavier |date=2020-08-01 |title=Machine learning approaches for the prediction of materials properties |url=http://aip.scitation.org/doi/10.1063/5.0018384 |journal=APL Materials |language=en |volume=8 |issue=8 |pages=080701 |doi=10.1063/5.0018384 |issn=2166-532X}}</ref><ref name="MullinTheLab21">{{Cite journal |last=Mullin, R. |date=28 March 2021 |title=The lab of the future is now |url=http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 |journal=Chemical & Engineering News |volume=99 |issue=11 |archiveurl=https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11 |archivedate=06 May 2022 |accessdate=16 February 2023}}</ref>
:chemical experimentation and molecular discovery labs<ref name="MullinTheLab21" /><ref name=":4">{{Cite journal |last=Burger |first=Benjamin |last2=Maffettone |first2=Phillip M. |last3=Gusev |first3=Vladimir V. |last4=Aitchison |first4=Catherine M. |last5=Bai |first5=Yang |last6=Wang |first6=Xiaoyan |last7=Li |first7=Xiaobo |last8=Alston |first8=Ben M. |last9=Li |first9=Buyi |last10=Clowes |first10=Rob |last11=Rankin |first11=Nicola |date=2020-07-09 |title=A mobile robotic chemist |url=https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org |journal=Nature |language=en |volume=583 |issue=7815 |pages=237–241 |doi=10.1038/s41586-020-2442-2 |issn=0028-0836}}</ref><ref name="LemonickExplore20">{{Cite journal |last=Lemonick, S. |date=06 April 2020 |title=Exploring chemical space: Can AI take us where no human has gone before? |url=https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 |journal=Chemical & Engineering News |volume=98 |issue=13 |archiveurl=https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13 |archivedate=29 July 2020 |accessdate=16 February 2023}}</ref>
:quantum physics labs<ref name="DoctrowArti19">{{cite web |url=https://www.pnas.org/post/podcast/artificial-intelligence-laboratory |title=Artificial intelligence in the laboratory |author=Doctrow, B. |work=PNAS Science Sessions |date=16 December 2019 |accessdate=16 February 2023}}</ref>


===Medical and recreational use===
*What's going on in these labs?
In its 2016 World Drug Report, the United Nations Office on Drugs and Crime estimated that 3.8 percent of the global population — roughly 183 million people — used cannabis in 2014.<ref name="UNODCWorld16">{{cite web |url=https://www.unodc.org/doc/wdr2016/WORLD_DRUG_REPORT_2016_web.pdf |format=PDF |title=World Drug Report 2016 |author=United Nations Office on Drugs and Crime |publisher=United Nations |isbn=9789210578622 |date=May 2016 |accessdate=27 January 2017}}</ref> Focusing in on the United States, the Substance Abuse and Mental Health Services Administration (SAMHSA) estimated that in 2014 the country had 35.1 million past-year marijuana users.<ref name="SAMHSAResults16">{{cite web |url=https://www.samhsa.gov/data/sites/default/files/NSDUH-DetTabs-2015/NSDUH-DetTabs-2015/NSDUH-DetTabs-2015.pdf |format=PDF |title=Results from The 2015 National Survey on Drug Use And Health: Detailed Tables |author=Substance Abuse and Mental Health Services Administration |editor=RTI International |page=178 |date=08 September 2016 |accessdate=27 January 2017}}</ref> It's not clear if that total includes legal medical marijuana users, which numbered roughly 2.4 million that year according to non-profit ProCon.org.<ref name="ProConNumber14">{{cite web |url=http://medicalmarijuana.procon.org/view.resource.php?resourceID=006445 |title=Number of Legal Medical Marijuana Patients (as of Oct. 27, 2014) |publisher=ProCon.org |date=27 October 2014 |accessdate=31 January 2017}}</ref> When compared to SAMHSA's estimate of 11.1 million users in 1997<ref name="RabkinDrug99">{{cite book |url=https://books.google.com/books?id=ZvzBdlolKrQC&pg=PA16 |title=Drug Control: DEA's Strategies and Operations in the 1990s |author=Rabkin, N.J. |publisher=U.S. General Accounting Office |year=July 1999 |pages=172 |isbn=9780788184833}}</ref>, it becomes clear that reported marijuana use has increased in the United States over the past few decades, whether it's through legalization efforts or otherwise. Some have pointed to the expansion of medical marijuana laws acting as gateways for increased adolescent use; however, multiple studies have have tried to but failed to find such a cause and effect relationship.<ref name="HasinMedical15">{{cite journal |title=Medical marijuana laws and adolescent marijuana use in the USA from 1991 to 2014: Results from annual, repeated cross-sectional surveys |journal=The Lancet Psychiatry |author=Hasin, D.S.; Wall, M.; Keyes, K.M. et al. |volume=2 |issue=7 |pages=601–608 |year=2015 |doi=10.1016/S2215-0366(15)00217-5}}</ref><ref name="WallAdol11">{{cite journal |title=Adolescent Marijuana Use from 2002 to 2008: Higher in States with Medical Marijuana Laws, Cause Still Unclear |journal=Annals of Epidemiology |author=Wall, M.M.; Poh, E.; Cerdá, M. |volume=21 |issue=9 |pages=714–716 |year=2011 |doi=10.1016/j.annepidem.2011.06.001 |pmid=21820632 |pmc=PMC3358137}}</ref><ref name="HarperDoMed12">{{cite journal |title=Do Medical Marijuana Laws Increase Marijuana Use? Replication Study and Extension |journal=Annals of Epidemiology |author=Harper, S.; Strumpf, E.C.; Kaufman, J.S. |volume=22 |issue=3 |pages=207–212 |year=2012 |doi=10.1016/j.annepidem.2011.12.002 |pmid=22285867}}</ref> Regardless, with 28 U.S. states now having some legalization law on the books<ref name="SteinmetzThese16" />, the number of marijuana users isn't likely to decrease any time soon.


===Cultivation and distribution===
:'''Materials science''': The creation of "a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions ..."<ref name=":2" />
On the Federal level, marijuana is considered a Schedule I drug and remains illegal.<ref name="GPO21USC812">{{cite web |url=https://www.gpo.gov/fdsys/pkg/USCODE-2011-title21/html/USCODE-2011-title21-chap13-subchapI-partB-sec812.htm |title=§812. Schedules of controlled substances |work=United States Code |publisher=U.S. Government Publishing Office |date=03 January 2012 |accessdate=27 January 2017}}</ref><ref name="LegerMari16" /> (Federal regulation of cannabis is discussed in detail later under "Regulatory scheme.") This doesn't leave a lot of options for researchers and the like. In fact, the only federally-granted grow operation (grow-op) as of this writing is the University of Mississippi, contracted by the National Institute on Drug Abuse (NIDA) to grow marijuana for approved research studies.<ref name="BajajHowThe14">{{cite web |url=https://takingnote.blogs.nytimes.com/2014/07/30/how-the-federal-government-slows-marijuana-research/ |title=How the Federal Government Slows Marijuana Research |author=Bajaj, V. |work=Taking Note: The New York Times |publisher=The New York Times Company |date=30 July 2014 |accessdate=25 January 2017}}</ref><ref name="CheslerGov15">{{cite web |url=http://weedrush.news21.com/government-restrictions-lack-of-funding-slow-progress-on-medical-marijuana-research/ |title=Government restrictions, lack of funding slow progress on medical marijuana research |author=Chesler, J.; Ard, A. |work=News21: America's Weed Rush |publisher=Carnegie Corporation of New York; John S. and James L. Knight Foundation |date=15 August 2015 |accessdate=25 January 2017}}</ref><ref name="WeissTestimony16" /><ref name="JosephDEA16">{{cite web |url=https://www.statnews.com/2016/08/10/marijuana-medical-research-dea/ |title=DEA decision keeps major restrictions in place on marijuana research |author=Joseph, A. |work=STAT |publisher=Boston Globe Media |date=10 August 2016 |accessdate=25 January 2017}}</ref><ref name="RudroffMari17">{{cite web |url=http://www.newsweek.com/marijuana-regulation-blocks-vital-ms-research-544886 |title=Marijuana Regulation Blocks Vital Multiple Sclerosis Research |author=Rudroff, T. |work=Newsweek |publisher=IBT Media, Inc |date=21 January 2017 |accessdate=25 January 2017}}</ref> (That may change for the better in 2017; see the next section on testing and research for more.)
:'''Materials science''': "Most of the applications of [machine learning (ML)] in chemical and materials sciences, as we have said, feature supervised learning algorithms. The goal there is to supplement or replace traditional modeling methods, at the quantum chemical or classical level, in order to predict the properties of molecules or materials directly from their structure or their chemical composition ... Our research group was applying the same idea on a narrower range of materials, trying to confirm that for a given chemical composition, geometrical descriptors of a material’s structure could lead to accurate predictions of its mechanical features."<ref name=":3" />
:'''Life science''': "In biological experiments, we generally cannot as easily declare victory, but we can use the systems biology approach of cycling between experimentation and modelling to see which sequences, when tested, are most likely to improve the model. In artificial intelligence, this is called active learning, and it has some similarity to the way in which we as humans learn as infants: we get some help from parents and teachers, but mainly model the world around us by exploring it and interacting with it. Ideally then, we would recreate such an environment for our machine learning algorithms in the laboratory, where we start with an initial ‘infant’ model of a certain regulatory system or protein function and let the computer decide what sequence designs to try out – a deep learning version of the ‘robot scientist’. Microbes are ideal organisms for such an approach, given the ease and speed with which they can be grown and genetically manipulated. Combined with laboratory automation, many microbial experiments can (soon) be performed with minimal human intervention, ranging from strain construction and screening, such as operated by Amyris, Gingko, Transcriptic, etc., to full-genome engineering or even the design of microbial ecologies."<ref name=":6" />
:'''Digital pathology''': "The collaboration combines two AI solutions, VistaPath’s Sentinel, the world’s first automated tissue grossing platform, and Gestalt’s AI Requisition Engine (AIRE), a leading-edge AI algorithm for accessioning, to raise the bar in AI-driven pathology digitization. Designed to make tissue grossing faster and more accurate, VistaPath’s Sentinel uses a high-quality video system to assess specimens and create a gross report 93% faster than human technicians with 43% more accuracy. It not only improves on quality by continuously monitoring the cassette, container, and tissue to reduce mislabeling and specimen mix-up, but also increases traceability by retaining original images for downstream review."<ref>{{Cite web |last=VistaPath |date=28 July 2022 |title=VistaPath Launches New Collaboration with Gestalt Diagnostics to Further Accelerate Pathology Digitization |work=PR Newswire |url=https://www.prnewswire.com/news-releases/vistapath-launches-new-collaboration-with-gestalt-diagnostics-to-further-accelerate-pathology-digitization-301594718.html |publisher=Cision US Inc |accessdate=17 February 2023}}</ref>
:'''Chemistry and molecular science''': "The benefits of combining automated experimentation with a layer of artificial intelligence (AI) have been demonstrated for flow reactors, photovoltaic films, organic synthesis, perovskites and in formulation problems. However, so far no approaches have integrated mobile robotics with AI for chemical experiments. Here, we built Bayesian optimization into a mobile robotic workflow to conduct photocatalysis experiments within a ten-dimensional space."<ref name=":4" />
:'''Chemistry and immunology''': "Chemistry and immunology laboratories are particularly well-suited to leverage machine learning because they generate large, highly structured data sets, Schulz and others wrote in a separate review paper. Labor-intensive processes used for interpretation and quality control of electrophoresis traces and mass spectra could benefit from automation as the technology improves, they said. Clinical chemistry laboratories also generate digital images—such as urine sediment analysis—that may be highly conducive to semiautomated analyses, given advances in computer vision, the paper noted."<ref name=":8">{{Cite web |last=Blum, K. |date=01 January 2023 |title=A Status Report on AI in Laboratory Medicine |work=Clinical Laboratory News |url=https://www.aacc.org/cln/articles/2023/janfeb/a-status-report-on-ai-in-laboratory-medicine |publisher=American Association for Clinical Chemistry |accessdate=17 February 2023}}</ref>
:'''Clinical research''': "... retrospective analysis of existing patient data for descriptive and clustering purposes [and] automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses ..."<ref name=":0" />
:'''Clinical research''': "AI ... offers a further layer to the laboratory system by analyzing all experimental data collected by experiment devices, whether it be a sensor or a collaborative robot. From data collected, AI is able to produce hypotheses and predict which combination of materials or temperature is desired for the experiment. In short, this system will allow scientists to be aided by a highly intelligent system which is constantly monitoring and analyzing the experimental output. In this way, AI will help an experiment from its inception to conclusion."<ref>{{Cite web |last=Chubb, P. |date=03 November 2020 |title=How disruptive technology is helping laboratories combat COVID-19 |url=https://datafloq.com/read/disruptive-technologies-lab-help-us-prepare-future-pandemics/ |publisher=Datafloq |accessdate=16 February 2023}}</ref>
:'''Clinical research/medical diagnostics''': "Artificial intelligence (AI) in the laboratory is primarily used to make sense of big data, the almost impossibly large sets of data that biologists and pharmaceutical R&D teams are accustomed to working with. AI algorithms can parse large amounts of data in a short amount of time and turn that data into visualizations that viewers can easily understand. In certain data-intensive fields, such as genomic testing and virus research, AI algorithms are the best way to sort through the data and do some of the pattern recognition work."<ref>{{Cite web |last=Stewart, B. |date=18 March 2021 |title=Using LIMS for Data Visualization |work=CSols Insights |url=https://www.csolsinc.com/insights/published-articles/using-lims-for-data-visualization/ |publisher=CSols, Inc |accessdate=17 February 2023}}</ref>
:'''Medical diagnostics''': Development and implementation of [[Clinical decision support system|clinical decision support systems]] <ref name=":0" /><ref name=":1" />
:'''Medical diagnostics''': "Finally, in the laboratory, AI reduces the number of unnecessary blood samples when diagnosing infection. Instead of the 'gold standard blood sample' that takes 24-72 hours, the algorithm can predict the outcome of the blood sample with almost 80% accuracy based on demographics, vital signs, medications, and laboratory and radiology results. These are all examples of how Artificial Intelligence can be used to test better and faster with information that already exists. This saves time and costs."<ref name=":5" />
:'''Medical diagnostics''': "Chang sees two overarching classes of AI models: those that tackle internal challenges in the lab, such as how to deliver more accurate results to clinicians; and those that seek to identify cohorts of patients and care processes to close quality gaps in health delivery systems. The lab, however, 'isn’t truly an island,' said Michelle Stoffel, MD, PhD, associate chief medical information officer for laboratory medicine and pathology at M Health Fairview and the University of Minnesota in Minneapolis. 'When other healthcare professionals are working with electronic health records or other applications, there could be AI-driven tools, or algorithms used by an institution’s systems that may draw on laboratory data.'"<ref name=":8" />
:'''Medical diagnostics''': AI is used for the formulation of reference ranges, improvement of quality control, and automated interpretation of results. "Continuous monitoring of specimen acceptability, collection and transport can result in the prompt identification and correction of problems, leading to improved patient care and a reduction in unnecessary redraws and delays in reporting results."<ref name=":13" />
:'''Reproduction science''': "The field of AI is the marriage of humans and computers while reproductive medicine combines clinical medicine and the scientific laboratory of embryology. The application of AI has the potential to disconnect healthcare professionals from patients through algorithms, automated communication, and clinical imaging. However, in the embryology laboratory, AI, with its focus on gametes and embryos, can avoid the same risk of distancing from the patient. Areas of application of AI in the laboratory would be to enhance and automate embryo ranking through analysis of images, the ultimate goal being to predict successful implantation. Might such a trend obviate the need for embryo morphological assessment, time-lapse imaging and preimplantation genetic testing for aneuploidy (PGT-A), including mosaicism. Additionally, AI could assist with automation through analysis of testicular sperm samples searching for viable gametes, embryo grading uniformity."<ref name=":9" />
:'''Chromatography-heavy sciences''': " A great example of this is AI in the Liquid Chromatography Mass Spectrometry (LC-MS) field. LC-MS is a great tool used to measure various compounds in the human body, including everything from hormone levels to trace metals. One of the ways AI has already integrated with LC-MS is how it cuts down on the rate limiting steps of LC-MS, which more often than not are sample prep and LC separations. One system that Physicians Lab has made use of is parallel processing using SCIEX MPX 2.0 High Throughput System. This system can couple parallel runs with one LCMS instrument, resulting in twice the speed with no loss to accuracy. It can do this by staggering two runs either using the same method, or different methods entirely. What really makes this system great is its ability to automatically detect carryover and inject solvent blanks to clean the instrument. The system will then continue its analyzing, while automatically reinjecting samples that may be affected by the carryover. It will also flag high concentration without user input, allowing for easy detection of possibly faulty samples. This allows it to operate without users from startup to shut down. Some of the other ways that it can be used to increase efficiency are by using integrated network features to work on anything from streamlining management to increased throughput."<ref name="AlbanoCal19" />
:'''Most any lab''': "Predictive analytics, for example, is one tool that the Pistoia Alliance is using to better understand laboratory instruments and how they might fail over time... With the right data management strategies and careful consideration of metadata, how to best store data so that it can be used in future AI and ML workflows is essential to the pursuit of AI in the laboratory. Utilizing technologies such as LIMS and ELN enables lab users to catalogue data, providing context and instrument parameters that can then be fed into AI or ML systems. Without the correct data or with mismatched data types, AI and ML will not be possible, or at the very least, could provide undue bias trying to compare data from disparate sources."<ref>{{Cite web |date=29 January 2021 |title=Data Analytics |work=Scientific Computing World - Building a Smart Laboratory 2020 |url=https://www.scientific-computing.com/feature/data-analytics-0 |publisher=Europa Science Ltd |accessdate=17 February 2023}}</ref>
:'''Most any lab''': "When the actionable items are automatically created by Optima, the 'engine' starts working. An extremely sophisticated algorithm is able to assign the tasks to the resources, both laboratory personnel and instruments, according to the system configuration. Optima, thanks to a large amount of time dedicated to research the best way to automate this critical process, is able to automate most of the lab resource scheduling."<ref>{{Cite web |last=Optima Team |date=15 December 2020 |title=The concept of machine learning applied to lab resources scheduling |work=Optima Blog |url=https://www.optima.life/blog/the-concept-of-machine-learning-applied-to-lab-resources-scheduling/ |publisher=Optima PLC Tracking Tools S.L |accessdate=17 February 2023}}</ref>


On the state level, the growth and distribution of cannabis depends on state law, which can vary from state to state. This topic is discussed further in the "Regulatory scheme" section, but here are the key points:
*A number of challenges exist in the realm of effectively and securely implementing AI in the laboratory. This includes:


* Federal law makes it illegal to cultivate and distribute marijuana, even in a state that has legalized such activity. However, while there's no legally-binding guarantee cultivators and distributors won't be pursued by federal law enforcement, they can limit their chances of such federal enforcement by following the letter of state and local law.
:Ethical and privacy challenges<ref name=":0" /><ref name=":8" /><ref name=":10" />
:Algorithmic limitations<ref name=":11" />
:Data access limitations, including "where to get it, how to share it, and how to know when you have enough to train a machine-learning system that will produce good results"<ref name=":11" /><ref name=":8" /><ref name=":14">{{Cite web |last=Sherwood, L. |date=10 February 2022 |title=SLAS 2022: Barriers remain to AI adoption in life sciences |work=LabPulse.com Showcasts |url=https://www.labpulse.com/showcasts/slas/2022/article/15300130/slas-2022-barriers-remain-to-ai-adoption-in-life-sciences |publisher=Science and Medicine Group |accessdate=17 February 2023}}</ref><ref name=":15">{{Cite journal |last=Bellini |first=Claudia |last2=Padoan |first2=Andrea |last3=Carobene |first3=Anna |last4=Guerranti |first4=Roberto |date=2022-11-25 |title=A survey on Artificial Intelligence and Big Data utilisation in Italian clinical laboratories |url=https://www.degruyter.com/document/doi/10.1515/cclm-2022-0680/html |journal=Clinical Chemistry and Laboratory Medicine (CCLM) |language=en |volume=60 |issue=12 |pages=2017–2026 |doi=10.1515/cclm-2022-0680 |issn=1434-6621}}</ref>
:Data integration and transformation issues<ref name=":0" /><ref name=":15" />
:Regulatory barriers<ref name=":11" /><ref name=":12" />
:Misaligned incentives<ref name=":11" />
:Lack of knowledgeable/skilled talent<ref name=":0" /><ref name=":8" /><ref name=":14" /><ref name=":15" />
:Cost of skilled talent and infrastructure for maintaining and updating AI systems<ref name=":8" />
:Legacy systems running outdated technologies<ref name=":14" />
:Lack of IT systems or specialized software systems<ref name=":15" />
:Lack of standardized, best practices-based methods of validating algorithms<ref name=":8" />
:Failure to demonstrate real-world performance<ref name=":12" />
:Failure to meet the needs of the professionals using it<ref name=":12" />


* Despite some of the variance of state laws, one aspect largely remains consistent: it's illegal to distribute marijuana over state lines, even when it's between two states with similar laws.<ref name="BelvilleCanI16">{{cite web |url=http://www.weednews.co/can-i-take-marijuana-from-one-legal-state-to-another/ |title=Can I Take Marijuana From One Legal State To Another? |work=Weed News |author=Belville, R. |publisher=WN Media, LLC |date=28 November 2016 |accessdate=31 January 2017}}</ref>
*Given those challenges, some considerations should be made about implementing AI-based components in the laboratory. Examples include:
 
* Regulations on the personal and commercial cultivation and distribution of marijuana vary significantly from state to state. Some states make a limited pool of available licenses available; others don't offer them or instead have specific alternative treatment or non-profit centers that handle growth and distribution.<ref name="FindLawMari">{{cite web |url=http://smallbusiness.findlaw.com/business-laws-and-regulations/marijuana-business-licenses-permits-and-planning.html |title=Marijuana Business: Licenses, Permits, and Planning |work=FindLaw |publisher=Thomson Reuters |accessdate=31 January 2017}}</ref><ref name="LeaflyHome">{{cite web |url=https://www.leafly.com/news/cannabis-101/home-cannabis-cultivation-laws-a-state-by-state-guide |title=Home Cannabis Cultivation Laws: A State-by-State Guide |work=Leafly - Cannabis 101 |publisher=Leafly Holdings, Inc |accessdate=31 January 2017}}</ref><ref name="ProCon28Legal16">{{cite web |url=http://medicalmarijuana.procon.org/view.resource.php?resourceID=000881 |title=28 Legal Medical Marijuana States and DC: Laws, Fees, and Possession Limits |work=ProCon.org |date=28 December 2016 |accessdate=31 January 2017}}</ref>
 
===Testing and research===
One area that continues to expand — while taking advantage of new scientific research and techniques — is the laboratory sphere, particularly in research, regulation, and standardization activities. According to July 2016 testimony from Susan R.B. Weiss, Division Director at the National Institute on Drug Abuse (NIDA), the National Institutes of Health (NIH) alone supported 281 cannabinoid research projects totally more than $111 million in 2015.<ref name="WeissTestimony16">{{cite web |url=https://www.hhs.gov/about/agencies/asl/testimony/2016-09/the-state-of-the-science-on-the-therapeutic-potential-of-marijuana-and-cannabinoids/index.html |title=Testimony from Susan R.B. Weiss, Ph.D. on The State of the Science on the Therapeutic Potential of Marijuana and Cannabinoids before Judiciary Committee |author=Weiss, S.R.B. |work=ASL Testimony |publisher=U.S. Department of Health & Human Services |date=13 July 2016 |accessdate=25 January 2017}}</ref>
 
While the research, analysis, and processing of cannabis has been ongoing for centuries<ref name="DeitchHemp03" />, it wasn't until 1896 that Wood ''et al.'' conducted one of the first documented chemical experiments to determine the constituents of cannabis. Several years later, the researchers were able to correctly identify the extracted and isolated cannabinol from the exuded resin of Indian hemp as C<sub>21</sub>H<sub>26</sub>O<sub>2</sub>.<ref name="WoodCann1899">{{cite journal |title=III.—Cannabinol. Part I |journal=Journal of the Chemical Society, Transactions |author=Wood, T.B.; Newton Spivey, W.T.; Easterfield, T.H. |volume=75 |pages=30–36 |year=1899 |doi=10.1039/CT8997500020}}</ref> As of mid-2015, 104 of the more than 750 constituents of ''Cannabis sativa'' have been identified as cannabinoids<ref name="RadwanIso15">{{cite journal |title=Isolation and pharmacological evaluation of minor cannabinoids from high-potency ''Cannabis sativa'' |journal=Journal of Natural Products |author=Radwan, M.M.; ElSohly, M.A.; El-Alfy, A.T. et al. |volume=78 |issue=6 |pages=1271-6 |year=2015 |doi=10.1021/acs.jnatprod.5b00065 |pmid=26000707 |pmc=PMC4880513}}</ref>, "a class of diverse chemical compounds that act on cannabinoid receptors in cells that modulate neurotransmitter release in the brain."<ref name="WHOTheHealth16">{{cite book |url=http://www.who.int/substance_abuse/publications/cannabis/en/ |title=The health and social effects of nonmedical cannabis use |author=World Health Organization |editor=Hall, W.; Renström, M.; Poznyak, V |publisher=World Health Organization |pages=95 |year=2016 |isbn=978921510240}}</ref>
 
Yet in the United States, when it comes to 1. enacting the broad level of testing required to ensure public safety — whether it be medical, recreational, or industrial use of cannabis — and 2. researching and better understanding the pharmacokinetics and pharmacodynamics (medical use and benefit) of cannabinoids in the human population, many have argued that laboratory testing of cannabis is still in its infancy<ref name="HazekampCanna12">{{cite journal |title=Cannabis - from cultivar to chemovar |journal=Drug Testing and Analysis |author=Hazekamp, A.; Fischedick, J.T. |volume=4 |issue=7–8 |pages=660–7 |year=2012 |doi=10.1002/dta.407 |pmid=22362625}}</ref><ref name="BushWorlds15">{{cite web |url=http://www.seattletimes.com/seattle-news/worldrsquos-strongest-weed-potency-testing-challenged/ |title=World’s strongest weed? Potency testing challenged |author=Bush, E. |work=The Seattle Times |publisher=The Seattle Times Company |date=18 February 2015 |accessdate=25 January 2017}}</ref><ref name="RutschQuality15">{{cite web |url=http://www.npr.org/sections/health-shots/2015/03/24/395065699/quality-testing-legal-marijuana-strong-but-not-always-clean |title=Quality-Testing Legal Marijuana: Strong But Not Always Clean |author=Rutsch, P. |work=Shots |publisher=National Public Radio |date=24 March 2015 |accessdate=25 January 2017}}</ref><ref name="KuzdzalUnrav15">{{cite journal |title=Unraveling the Cannabinome |journal=The Analytical Scientist |author=Kuzdzal, S.; Lipps, W. |issue=0915 |year=2015 |url=https://theanalyticalscientist.com/issues/0915/unraveling-the-cannabinome/ |accessdate=19 January 2017}}</ref><ref name="CrombieMari16">{{cite web |url=http://www.oregonlive.com/marijuana/index.ssf/2016/07/marijuana_labs_prepping_for_st.html |title=Marijuana labs prepping for regulation and oversight; no lab licenses issued yet |author=Crombie, N. |work=The Oregonian |publisher=Oregon Live LLC |date=25 July 2016 |accessdate=25 January 2017}}</ref><ref name="KuzdzalACloser16">{{cite web |url=http://event.lvl3.on24.com/event/13/38/14/4/rt/1/documents/resourceList1484589923854/emerging_cannabis_industry_whitepaper.pdf |archiveurl=http://web.archive.org/web/20170210234439/http://event.lvl3.on24.com/event/13/38/14/4/rt/1/documents/resourceList1484589923854/emerging_cannabis_industry_whitepaper.pdf |format=PDF |title=A Closer Look at Cannabis Testing |author=Kuzdzal, S.; Clifford, R.; Winkler, P.; Bankert, W. |publisher=Shimadzu Corporation |date=December 2016 |archivedate=19 January 2017 |accessdate=19 January 2017}}</ref> and evidence-based research of marijuana continues to be slow and bogged down in regulation.<ref name="BajajHowThe14" /><ref name="CheslerGov15" /><ref name="WeissTestimony16" /><ref name="JosephDEA16" /><ref name="RudroffMari17" /> In regards to the first issue, as some form of legalization continues to sweep across states, regulators, users, and industry are recognizing the need for improved standardization of the production and testing of medical and recreational marijuana; the current state of improper labeling and potentially harmful contaminants<ref name="HazekampCanna12" /><ref name="BushWorlds15" /><ref name="RutschQuality15" /><ref name="KuzdzalACloser16" /> will only serve to hinder the industry. To the second issue, some within the federal government seem to recognize the roadblocks to improved evidence-based research and are working to slowly improve how researchers can legally acquire and test marijuana in the U.S.<ref name="WeissTestimony16" /><ref name="JosephDEA16" /><ref name="Romza-KutzTheSilver16">{{cite web |url=http://www.thompsoncoburn.com/insights/blogs/tracking-cannabis/post/2016-08-15/the-silver-lining-in-the-dea-s-refusal-to-reclassify-cannabis |title=The silver lining in the DEA’s refusal to reclassify cannabis |work=Tracking Cannabis |author=Romza-Kutz, D.; Roth V, F. |publisher=Thompson Coburn LLP |date=15 August 2016 |accessdate=25 January 2017}}</ref>
 
An excerpt from the previously mentioned testimony of NIDA's Dr. Weiss summates this well:
 
<blockquote>The current state of the research on marijuana and its constituent cannabinoids suggests the potential for therapeutic value for a number of conditions; however, more evidence is needed before marijuana or cannabinoid products (beyond those already approved through the Food and Drug Administration [FDA]) are ready for medical use. Promising preclinical findings do not always prove to be clinically relevant, and even fewer lead to new treatments. Moreover, clinical studies of sufficient quality to meet FDA standards for drug approval are currently lacking for most conditions. Among the factors that impact this research are the specific statutory requirements and treaty obligations that govern research on marijuana.  NIH is working closely with the Office of National Drug Control Policy (ONDCP), the Drug Enforcement Administration (DEA), and FDA to explore ways to streamline these processes to facilitate research.<ref name="WeissTestimony16" /></blockquote>
 
In the meantime, government entities such as the National Institutes of Health and non-profits such as jCanna push forward with scientific conferences, summits, and roundtables that bring scientists and interested parties together to share existing knowledge and testing techniques.<ref name="NIHMari16">{{cite web |url=http://apps1.seiservices.com/nih/mj/2016/ |title=The Marijuana and Cannabinoids: A Neuroscience Research Summit |publisher=National Institutes of Health |date=23 March 2016 |accessdate=25 January 2017}}</ref><ref name="jCannaCSC17">{{cite web |url=https://www.cannabisscienceconference.com/ |title=Cannabis Science Conference |publisher=jCanna, Inc |accessdate=25 January 2017}}</ref>
 
===Other areas of concern===
When examining the current U.S. cannabis industry, it's important to note a few additional areas of concern that impact it, again tightly linked to federal regulations (which are discussed extensively in the next section): banking and advertising. Issues related to both of these topics continue to limit how state-based grow-ops (grow operations), dispensaries, and testing laboratories are funded and operated.
 
====Banking====
Since the U.S. federal government still considers marijuana to be illegal, by extension banks and credit unions — which are regulated by a patchwork collection of federal (and state) laws — put themselves into potentially dangerous territory by accepting money from depositors engaging in federally illegal activities; the bank can be punished by federal institutions such as that Federal Deposit Insurance Corporation (FDIC).<ref name="HillBanks15">{{cite journal |title=Banks, Marijuana, and Federalism |journal=Case Western Reserve Federal Law Review |author=Hill, J.A. |volume=65 |issue=3 |pages=597–647 |year=2015 |url=http://scholarlycommons.law.case.edu/caselrev/vol65/iss3/7}}</ref> In an attempt to ease concerns of industry and banks in states that had implements legalization efforts, the Treasury Department's Financial Crimes Enforcement Network (FinCEN) released a guidance document in February 2014 that "does not grant immunity from prosecution or civil penalties to banks that serve legal marijuana businesses" but rather "directs prosecutors and regulators to give priority to cases only where financial institutions have failed to adhere to the guidance."<ref name="KovaleskiUS14">{{cite web |url=https://www.nytimes.com/2014/02/15/us/us-issues-marijuana-guidelines-for-banks.html |title=U.S. Issues Marijuana Guidelines for Banks |author=Kovaleski, S.F. |work=The New York Times |publisher=The New York Times Corporation |date=14 February 2014 |accessdate=25 January 2017}}</ref><ref name="FinCEN_BSA14">{{cite web |url=https://www.fincen.gov/resources/statutes-regulations/guidance/bsa-expectations-regarding-marijuana-related-businesses |title=BSA Expectations Regarding Marijuana-Related Businesses |author=Financial Crimes Enforcement Network |publisher=U.S. Department of the Treasury |date=14 February 2014 |accessdate=25 January 2017}}</ref> However, the guidance has remained just that: guidance; it doesn't prevent federal law enforcement or regulating agencies from taking action. An August 2016 attempt to reclassify marijuana into a lower classification than Schedule I failed<ref name="LegerMari16" /><ref name="JosephDEA16" />, keeping the FinCEN guidance in place as a recommendation for how federal authorities should enforce existing law.
 
According to an Associated Press report in April 2016, the guidance has had some sort of impact, with banks and credit unions willing to handle any money associated with marijuana increasing from 51 in March 2014 to 301 in March 2016.<ref name="HansenBanking16">{{cite web |url=http://bigstory.ap.org/article/804ae396daab4ba98f814b186f872ef6/banking-woes-easing-some-legal-pot-businesses |title=Banking woes easing for some legal pot businesses |author=Hansen, K.; Johnson, G. |work=Associated Press: The Big Story |publisher=Associated Press |date=20 April 2016 |accessdate=25 January 2017}}</ref> However, this hasn't prevented those in states with newly minted medical and recreational marijuana legalization laws from being worried about how cannabis money will be handled, particularly with the new Trump administration taking the reigns of government. California, which in November 2016 legalized recreational use of marijuana beginning in 2018, has already petitioned that administration to clarify it's policy early on. "We have a year to develop a system that works in California and which addresses the many issues that exist as a result of the federal-state legal conflict," wrote California Treasurer John Chiang to Trump. "Uncertainty about the position of your administration creates even more of a challenge."<ref name="BloodCali16">{{cite web |url=http://bigstory.ap.org/article/d54ea614db274238986a8e0d77dbb147/california-treasurer-asks-trump-guidance-pot-banking |title=California treasurer asks Trump for guidance on pot, banking |author=Blood, M.R. |work=Associated Press: The Big Story |publisher=Associated Press |date=02 December 2016 |accessdate=25 January 2017}}</ref>
 
Similar legalization changes in Massachusetts prompted its senator Elizabeth Warren, along with nine other senators, to write to FinCEN in early 2017 requesting even clearer, more friendly guidance for marijuana vendors.<ref name="LeBlancUSSen17">{{cite web |url=https://lasvegassun.com/news/2017/jan/02/us-sen-warren-seeks-to-pull-pot-shops-out-of-banki/ |title=US Sen. Warren seeks to pull pot shops out of banking limbo |author=LeBlanc, S. |work=Las Vegas Sun |publisher=Greenspun Media Group |date=02 January 2017 |accessdate=25 January 2017}}</ref> Yet it remains to be seen if entities outside of grow-ops and dispensaries will see banking relief. In particular, testing laboratories continue to struggle with managing cash flow and acquiring bank lending for their operations<ref name="LampachQA13">{{cite web |url=https://mjbizdaily.com/qa-with-steep-hill-lab-ceo-david-lampach-cannabis-testing-market-could-hit-40m-in-2-years/ |title=Q&A With CEO of Steep Hill Halent: US Cannabis Testing Market Could Hit $40M by 2016 |work=Marijuana Business Daily |author=Lampach, D. |publisher=Anne Holland Ventures, Inc |date=20 November 2013 |accessdate=25 January 2017}}</ref><ref name="MartinProfit16">{{cite web |url=http://www.cacannabislabs.com/ |title=Profitability in the Cannabis Laboratory Industry |work=Association of Commercial Cannabis Laboratories |author=Martin, R.W. |publisher=Association of Commercial Cannabis Laboratories |date=May 2016 |accessdate=25 January 2017}}</ref><ref name="TulsiABright16">{{cite web |url=http://www.labmanager.com/research-specific-labs/2016/10/today-s-cannabis-research-market |title=A Bright Future for Cannabis Testing Services |work=Lab Manager |author=Tulsi, B.B. |publisher=LabX Media Group |date=03 October 2016 |accessdate=25 January 2017}}</ref>, causing some to believe consolidation of such labs will occur before the industry can really even take off.<ref name="LampachQA13" /><ref name="DigiPathAUnique16">{{cite web |url=http://digipath.com/wp-content/uploads/2016/10/Digipath-Company-Report.pdf |format=PDF |title=A Unique Investment Vehicle in Laboratory Testing |publisher=DigiPath, Inc |date=October 2016 |pages=36 |accessdate=25 January 2017}}</ref>
 
====Advertising====
Advertising of marijuana products is another area of concern, though the regulations and laws regarding it are less clear. When it comes to television and radio broadcasting and its associated advertising, a federally-granted broadcasting license stands to be lost care of the Federal Communications Commission (FCC). The trouble is, it's not clear if the FCC would act against broadcasters; the FCC hasn't issued guidance in the same way FinCEN has. "I don’t think anybody knows, and that’s the problem," said California Broadcasters Association President Joe Berry in an August 2016 report published by the The Sacramento Bee. "Without a clear indication [from the FCC on marijuana advertising], the vast majority of broadcasters are going to stay away from this issue."<ref name="WhiteIfCali16">{{cite web |url=http://www.sacbee.com/news/politics-government/capitol-alert/article96040082.html |title=If California legalizes pot, will TV ads be far behind? |author=White, J.B. |work=The Sacramento Bee |publisher=The McClatchy Company |date=17 August 2016 |accessdate=25 January 2017}}</ref> California, of course, made recreational marijuana legal, and its proposed law sought to address the issue of advertising, including "a provision restricting TV and radio ads so they are not targeted to minors," while also addressing the authority of the FCC to enforce regardless.<ref name="McGreevyQA16">{{cite web |url=http://www.latimes.com/politics/la-pol-sac-proposition-64-marijuana-legalization-qa-20161030-snap-20161029-story.html |title=Q&A: Proposition 64 would legalize recreational use of marijuana though it's illegal under federal law. How will that work? |author=McGreevy, P. |work=Los Angeles Times |publisher=tronc, Inc |date=30 October 2016 |accessdate=25 January 2017}}</ref>
 
Other forms of advertising also remain problematic. In late November 2015, the United States Postal Service (USPS) out of Portland, Oregon published its interpretation of federal law regarding "mailpieces containing advertisements about marijuana," regarding it illegal to distribute certain forms of marijuana advertisement, citing 21 U.S. Code § 843(c).<ref name="ReinThePot15">{{cite web |url=https://www.washingtonpost.com/news/federal-eye/wp/2015/12/21/the-pot-business-may-be-legal-but-newspapers-cant-run-ads-for-it-the-u-s-postal-service-says/ |title=The pot business may be legal, but newspapers can’t run ads for it, the U.S. Postal Service says |author=Rein, L. |work=The Washington Post |publisher=WP Company, LLC |date=21 December 2015 |accessdate=25 January 2017}}</ref> The U.S. Patent and Trademark Office (PTO) has, controversially, also gotten involved, stating that trademarking of a "brand controlled substances or related paraphernalia that are illegal to possess or sell" legally doesn't fit within a trademark's commercial viability because at the federal level marijuana is not legal for commerce.<ref name="OxenfordAccepting16">{{cite web |url=http://www.broadcastlawblog.com/2016/12/articles/accepting-advertising-for-marijuana-or-marijuana-paraphernalia-the-trademark-office-rules-on-a-related-issue-that-provides-more-reason-for-caution/ |title=Accepting Advertising for Marijuana or Marijuana Paraphernalia: The Trademark Office Rules on a Related Issue that Provides More Reason For Caution |author=Oxenford, D. |work=Broadcast Law Blog |date=13 December 2016 |accessdate=25 January 2017}}</ref> (Legal experts such as Dariush Adli suggest "creative ways" of getting around this, from registering trademarks in multiple states to registering "non-cannabis merchandise in order to generate some federal protection for their mark."<ref name="AdliObtain16">{{cite web |url=http://adlilaw.blogspot.com/2016/12/obtaining-trademark-protection-for_9.html |title=Obtaining Trademark Protection for Cannabis Businesses |author=Adli, D. |publisher=ADLI Law Group |date=21 December 2016 |accessdate=25 January 2017}}</ref>) Even billboards are an issue, with state lawmakers proposing new regulations on marijuana advertising on them weeks after the state passed its recreational legalization laws.<ref name="McGreevyPotAds16">{{cite web |url=http://www.latimes.com/politics/la-pol-ca-pot-ads-snap-20161221-story.html |title=Pot ads along highways? Lawmakers wrangle over legalization's consequences |author=McGreevy, P. |work=Los Angeles Times |publisher=tronc, Inc |date=21 December 2016 |accessdate=25 January 2017}}</ref> And state laws, such as those found in Alaska, can create their own set of challenges in staying legal with marijuana advertising.<ref name="AndrewsGaps16">{{cite web |url=https://www.adn.com/alaska-marijuana/2016/12/26/gaps-in-alaska-marijuana-advertising-rules-cause-worry/ |title=Gaps in Alaska marijuana ad rules cause worry |author=Andrews, L. |work=Alaska Dispatch News |publisher=Alaska Dispatch Publishing |date=27 December 2016 |accessdate=25 January 2017}}</ref>
 
Despite all this, at least one financial consultant believes marijuana marketing will become more prevalent: GreenWave Advisors' Matthew Karnes estimates spending will jump to $75 million by 2021.<ref name="StilsonWhyMari17">{{cite web |url=http://www.adweek.com/news/advertising-branding/why-marijuana-marketing-will-be-bigger-ever-year-175246 |title=Why Marijuana Marketing Will Be Bigger Than Ever This Year |author=Stilson, J. |work=Adweek |publisher=Adweek, LLC |date=03 January 2017 |accessdate=25 January 2017}}</ref>
 
==Regulation and standardization==
===Federal regulation of cannabis===
On October 27, 1970, the Controlled Substances Act put into place five schedules or classifications of drugs that would be regulated in some fashion, and drugs were initially classified into those schedules followed by annual reviews and updates.<ref name="GPO21USC812" /> Marijuana was initially placed under Schedule I<ref name="GPO21USC812" /> and remains there today.<ref name="LegerMari16" /><ref name="JosephDEA16" /><ref name="GrubbsDEA16">{{cite web |url=http://www.cnsnews.com/news/article/alex-grubbs/dea-declines-request-reclassify-marijuana-citiing-its-high-potential-abuse |title=DEA Declines Request to Reclassify Marijuana, Citiing Its 'High Potential for Abuse' |author=Grubbs, A. |work=CNSNews |publisher=Media Research Center |date=13 August 2016 |accessdate=27 January 2017}}</ref> As a Schedule I drug, the federal government is indicating marijuana has<ref name="GPO21USC812" />:
 
* "a high potential for abuse";
* "no currently accepted medical use in treatment in the United States"; and
* "a lack of accepted safety for use of the drug or other substance under medical supervision".
 
Then came the Reorganization Plan No. 2 of 1973, which took existing enforcement entities such as the Bureau of Narcotics and Dangerous Drugs and placed them into a new, unified entity called the Drug Enforcement Administration (DEA).<ref name="GPO5USCApp">{{cite web |url=https://www.gpo.gov/fdsys/pkg/USCODE-2011-title5/html/USCODE-2011-title5-app-reorganiz-other-dup96.htm |title=Reorganization Plan No. 2 of 1973 |work=United States Code |publisher=U.S. Government Publishing Office |date=03 January 2012 |accessdate=27 January 2017}}</ref><ref name="DEAHist7075">{{cite web |url=https://www.dea.gov/about/history/1970-1975.pdf |format=PDF |title=Drug Enforcement Administration: 1970–1975 |work=DEA History In Depth |publisher=U.S. Drug Enforcement Administration |accessdate=27 January 2017}}</ref> Then President Richard Nixon said of the transition<ref name="GPO5USCApp" />:
 
<blockquote>The enforcement work could benefit significantly, however, from consolidation of our anti-drug forces under a single unified command. Right now the Federal Government is fighting the war on drug abuse under a distinct handicap, for its efforts are those of a loosely confederated alliance facing a resourceful, elusive, worldwide enemy.</blockquote>
 
The DEA was given numerous responsibilities, including but not limited to the development of enforcement strategy; investigation and prosecution preparation of suspects violating federal law; regulation of drugs and other controlled substances; and coordination and cooperation with state and local government drug enforcement efforts.<ref name="GPO5USCApp" /> Since then the DEA has taken various steps — with guidance from the Food and Drug Administration (FDA)<ref name="HamiltonTheFDA16">{{cite web |url=https://news.vice.com/article/dea-fda-marijuana-schedule-1-status-decision |title=The FDA told the DEA whether pot is medicine — but it won't tell the public |author=Hamilton, K. |work=Vice News |publisher=Vice Media, LLC |date=27 June 2016 |accessdate=27 January 2017}}</ref> — to regulate and enforce the availability and use of controlled substances such as marijuana. As the decriminalization and legalization efforts of states have increased in past decades, this has brought federal regulation and enforcement conflicts to those states that have decriminalized and legalized, largely due to the federal government's insistence in maintaining marijuana as a Schedule I drug.<ref name="Romza-KutzTheSilver16" /><ref name="HudakTheCon15">{{cite web |url=http://www.newsweek.com/conflict-between-federal-and-state-marijuana-laws-claims-victim-345099 |title=The Conflict Between Federal and State Marijuana Laws Claims a Victim |author=Hudak, J. |work=Newsweek |publisher=Newsweek, LLC |date=20 June 2015 |accessdate=27 January 2017}}</ref>
 
Numerous changes in policy and controversies have occurred since the Controlled Substance Act and DEA were implemented, including a 22-year-long effort by the National Organization for the Reform of Marijuana Laws (NORML) to have marijuana rescheduled (1972–1994).<ref name="DPAMAPSTheDEA14">{{cite web |url=http://www.drugpolicy.org/sites/default/files/DPA-MAPS_DEA_Science_Final.pdf |format=PDF |title=The DEA: Four Decades of Impeding And Rejecting Science |author=Drug Policy Alliance; MAPS |publisher=Drug Policy Alliance |date=June 2014 |accessdate=27 January 2017}}</ref> However, the following talking points represent the most recent important federal-level changes and rulings that impact federal regulation of and enforcement of laws relating to cannabis.
 
'''October 19, 2009: The Ogden Memorandum'''
 
Deputy Attorney General David W. Ogden issued a memorandum "intended solely as a guide to the exercise of investigative and prosecutorial discretion" in regards to state-based laws allowing medical cannabis.<ref name="OgdenMemor09">{{cite web |url=https://www.justice.gov/opa/blog/memorandum-selected-united-state-attorneys-investigations-and-prosecutions-states |title=Memorandum for Selected United State Attorneys on Investigations and Prosecutions in States Authorizing the Medical Use of Marijuana |author=Ogden, D.W. |work=Justice Blogs |publisher=Department of Justice |date=19 October 2009 |accessdate=26 January 2017}}</ref> The guidance essentially told U.S. attorneys to not prosecute those entities complying fully with state cannabis laws. Researchers generally agree that this memo acted "as a catalyst for expansion of [state-sanctioned and gray market] cannabis supply in states with poorly defined regulations," though the degree to which it influenced such growth remains poorly documented and requires further investigation.<ref name="CambronState16">{{cite journal |title=State and National Contexts in Evaluating Cannabis Laws: A Case Study of Washington State |journal=Journal of Drug Issues |author=Cambron, C.; Guttmannova, K.; Fleming, C.B. |volume=47 |issue=1 |pages=74–90 |year=2017 |doi=10.1177/0022042616678607}}</ref> To be sure, it likely had some effect, as the number of licensed patients using medical marijuana in the state of Colorado increased from 4,800 in 2008 to 41,000 in 2009, and operating dispensaries jumped to more than 900 by mid-2010.<ref name="HIDTATheLeg13">{{cite web |url=http://www.rmhidta.org/html/final%20legalization%20of%20mj%20in%20colorado%20the%20impact.pdf |format=PDF |title=The Legalization of Marijuana in Colorado: The Impact |author=Rocky Mountain HIDTA |volume=1 |date=August 2013 |accessdate=26 January 2017}}</ref>
 
'''June 29, 2011: The Cole Memorandum 1'''
 
Deputy Attorney General James M. Cole issued a memo as a follow-up to the Ogden Memo, muddying the waters in the process. While stating that the stance of efficiently using department resources as outlined in the Ogden Memo still stood, Cole also made it clear that large grow-ops that didn't qualify as "caregivers" had sprung up since.<ref name="ColeMemo11">{{cite web |url=https://www.justice.gov/sites/default/files/oip/legacy/2014/07/23/dag-guidance-2011-for-medical-marijuana-use.pdf |format=PDF |title=Memorandum for United States Attorneys |author=Cole, J.M. |publisher=Department of Justice |date=29 June 2011 |accessdate=26 January 2017}}</ref> The language of the memo essentially said "get off your butts and nail those suckers."<ref name="GreenfieldTheCole13">{{cite web |url=https://blog.simplejustice.us/2013/08/30/the-cole-memo-2-0-this-changes-everything/ |title=The Cole Memo 2.0: This Changes Everything |work=Simple Justice |author=Greenfield, S.H. |date=30 August 2013 |accessdate=26 January 2017}}</ref> Cambron ''et al.''<ref name="CambronState16" /> and Fairman<ref name="FairmanTrends16">{{cite journal |title=Trends in registered medical marijuana participation across 13 US states and District of Columbia |journal=Drug and Alcohol Dependence |author=Fairman, B.J. |volume=159 |pages=72–9 |year=2016 |doi=10.1016/j.drugalcdep.2015.11.015 |pmid=26686277}}</ref> suggest this memo had some impact as evidenced by declines in cannabis patient registration from 2011–2013 in Colorado, Michigan, and Montana.
 
'''August 29, 2013: The Cole Memorandum 2'''
 
Deputy Attorney General James M. Cole issued a follow-up memo to his original two years later, following 1. on the heels of then President Obama reiterating publicly that the Department of Justice (DoJ) was to not focus in unnecessarily on states that have passed legalization laws and 2. Washington and Colorado legalizing recreational use of cannabis.<ref name="CambronState16" /> The second memorandum sought to reduce the emphasis on the size of the grow-op and increase emphasis on — by a case-by-case basis — "whether the operation is demonstrably in compliance with a strong and effective state regulatory system."<ref name="ColeMemo13">{{cite web |url=https://www.justice.gov/iso/opa/resources/3052013829132756857467.pdf |format=PDF |title=Memorandum for All United States Attorneys |author=Cole, J.M. |publisher=Department of Justice |date=29 August 2013 |accessdate=26 January 2017}}</ref> The memo also clarified specific cases where federal enforcement would be warranted, including distribution to minors, interstate transport, and preventing drugged driving (though it didn't state how). Generally speaking, states saw little federal intervention except in the case of state law being broken or requiring dispensaries to move further away from schools.<ref name="MPPFederal16">{{cite web |url=https://www.mpp.org/federal/federal-enforcement-policy-on-state-marijuana-laws/ |title=Federal Marijuana Enforcement Policy |publisher=Marijuana Policy Project |date=2016 |accessdate=26 January 2017}}</ref><ref name="CambronState16" /> Despite the memo, some attorneys continued to see Cole Memorandum 2 as nothing more than unclear language that had no legal weight for anxious growers and distributors in states where cannabis was legalized.<ref name="GreenfieldTheCole13" />
 
'''December 16, 2014 to current: Rohrabacher-Farr Amendment'''
 
A tenuous truce of sorts arrived with the passage of the Rohrabacher-Farr Amendment in December 2014. The amendment prohibited the DoJ from spending funds to prevent or enforce against state laws that allow for medical marijuana cultivation, distribution, and use, particularly when those actions are performed consistent with state laws.<ref name="ArmentanoPres14">{{cite web |url=http://blog.norml.org/2014/12/16/president-to-sign-federal-spending-bill-protecting-state-sanctioned-medical-marijuana-programs/ |title=President Signs Federal Spending Bill Protecting State Sanctioned Medical Marijuana Programs |author=Armentano, P. |work=NORML Blog |publisher=NORML Foundation |date=16 December 2014 |accessdate=27 January 2017}}</ref> Before being passed in December 2014, the amendment had actually been introduced and defeated six times in the previous 10 years.<ref name="BrekkeHouse14">{{cite web |url=https://ww2.kqed.org/news/2014/05/30/house-votes-to-block-medical-pot-prosecution/ |title=House Votes to End Medical Marijuana Prosecutions |author=Brekke, D. |work=KQED News |publisher=KQED, Inc |date=30 May 2014 |accessdate=27 January 2017}}</ref> The DoJ later went on to challenge the amendment on several occasions, from ''U.S. v. Marin Alliance for Medical Marijuana'' in 2015 to a collection of 10 different cases from California and Washington in 2016. In both cases, the courts ruled against the DoJ, setting precedent against further department action.<ref name="IngrahamFed15">{{cite web |url=https://www.washingtonpost.com/news/wonk/wp/2015/10/20/federal-court-tells-the-dea-to-stop-harassing-medical-marijuana-providers/ |title=Federal court tells the DEA to stop harassing medical marijuana providers |author=Ingraham, C. |work=The Washington Post |publisher=WP Company, LLC |date=20 October 2015 |accessdate=27 January 2017}}</ref><ref name="FermerTheLargest16">{{cite web |url=http://www.huffingtonpost.com/entry/medical-marijuana-9th-circuit_us_57b36a31e4b04ff883990337 |title=The Largest Federal Appeals Court Tells DOJ To Back Off State-Legal Medical Marijuana |author=Fermer, M. |work=The Huffington Post |publisher=TheHuffingtonPost.com, Inc |date=16 August 2016 |accessdate=27 January 2017}}</ref>
 
One of the downsides of Rohrabacher-Farr is that it has essentially acted as a short-term rider attached to several spending bills since December 2014. As of January 2017, it was last renewed through April 2017; however, with the new Trump administration coalescing — including Trump's demonstrably anti-drug U.S. Attorney General nominee Jeff Sessions — some in the industry are concerned the amendment may not be renewed, opening the door again for the DoJ to implement stronger enforcement.<ref name="SteinmetzThese16" /><ref name="SwerdlowApril17">{{cite web |url=http://www.marijuananews.org/afraid |title=April 28, 2017 - Be Afraid, Be Very Afraid + IRS FU |author=Swerdlow, L. |publisher=Marijuana Anti-Prohibition Project |date=22 January 2017 |accessdate=27 January 2017}}</ref><ref name="YakowiczThePerfect17">{{cite web |url=http://www.inc.com/will-yakowicz/perfect-storm-regulations-could-cripple-marijuana-industry.html |title=The Perfect Storm That Could Cripple the Marijuana Industry Under Trump |author=Yakowicz, W. |work=Inc |publisher=Mansueto Ventures, LLC |date=20 January 2017 |accessdate=27 January 2017}}</ref><ref name="RohrabacherRohr17">{{cite web |url=https://rohrabacher.house.gov/media-center/press-releases/rohrabacher-praises-sessions-urges-ag-nominee-to-respect-federal |title=Rohrabacher praises Sessions, urges AG nominee to respect federal marijuana law |author=Rohrabacher, D. |publisher=State of California |date=11 January 2017 |accessdate=27 January 2017}}</ref>
 
'''August 11, 2016: DEA denies petition to reschedule marijuana out of Schedule I'''
 
A request made by two governors and a psychiatric nurse practitioner to the DEA asking it to reschedule marijuana into any other schedule other than Schedule I was denied, as had been done with previous attempts in 2009 and 2011.<ref name="LegerMari16" /><ref name="JosephDEA16" /><ref name="GrubbsDEA16" /> Reasons included known health issues such as prenatal exposure and negative impacts on several biological systems as well as limited research and new drug applications.<ref name="Romza-KutzTheSilver16" /> At the same time, however, the DEA also recognized the need for further research and the lack of legal marijuana sources for researchers, publishing a policy statement stating intent "to increase the lawful supply of marijuana available to researchers."<ref name="81FR53846">{{cite journal |url=https://www.federalregister.gov/documents/2016/08/12/2016-17955/applications-to-become-registered-under-the-controlled-substances-act-to-manufacture-marijuana-to |journal=Federal Register |title=Applications To Become Registered Under the Controlled Substances Act To Manufacture Marijuana To Supply Researchers in the United States |volume=81 |issue=156 |date=12 August 2016 |pages=53846–8 |accessdate=27 January 2017}}</ref>
 
===State and local regulation===
As of January 2017, 28 states have some sort of broad decriminalization or legalization laws for cannabis on the books.<ref name="SteinmetzThese16" /> In October 1973, Oregon became the first state to enact decriminalization laws for marijuana, imposing a $100 fine for possession of less than an ounce. Eleven other states followed a similar path within five years.<ref name="SingleTheImp81">{{cite book |chapter=The Impact of Marijuana Decriminalization |title=Research Advances in Alcohol and Drug Problems |author=Single, E.W. |editor=Israel, Y.; Glaser, F.B.; Kalant, H. et al. |publisher=Springer US |year=1981 |pages=405–424 |doi=10.1007/978-1-4615-7740-9_12 |isbn=9781461577409}}</ref> The next wave of changes began with the passage of medical marijuana legislation in California — the Compassionate Use Act — in November 1996, followed by similar legislation in Oregon and Alaska in 1998, Maine in 1999, and Colorado, Hawaii, and Nevada in 2000.<ref name="CambronState16" /><ref name="Alaska98Results">{{cite web |url=http://www.elections.alaska.gov/results/98GENR/results.htm |title=Election Summary Report, State of Alaska 1998 General Election: Official Results |work=Election Results |publisher=State of Alaska Division of Elections |date=1 December 1998 |accessdate=1 February 2017}}</ref> Other states continued to add decriminalization and medical marijuana laws in the 2000s. But it wasn't until 2012 that Colorado and Washington became the first states to make recreational marijuana legal, followed by Alaska, Oregon, and the District of Columbia in 2014.<ref name="CambronState16" /> Colorado, Maine, Massachusetts, and Nevada followed suit in 2016.<ref name="BurkeFour17">{{cite web |url=http://www.natlawreview.com/article/four-more-states-pass-new-marijuana-laws-california-maine-massachusetts-nevada |title=Four More States Pass New Marijuana Laws: California, Maine, Massachusetts, Nevada |author=Burke, C. |work=National Law Review |publisher=National Law Forum, LLC |date=04 January 2017 |accessdate=01 February 2017}}</ref>
 
As shown by Cambron ''et al.'' in 2016 (before the November election results)<ref name="CambronState16" />, dispensaries, possession limits, and interstate ID card acceptance can vary significantly among affected states. California, Colorado, Michigan, Oregon, and Washington lead in number of dispensaries; Massachusetts, Oregon, and Washington in maximum possession limits; and Arizona plus five others allowed ID cards from other states. Yet allowed dispensaries can number in the single digits, possession limits can be as low as one ounce, and numerous states still don't honor ID cards from other states.<ref name="CambronState16" />
 
Then there's the matter of state differences in testing, enforcement, advertising allowances, etc. It helps to turn to professional associations and organizations — who often lead the charge for improved, more relevant standards — to sort through the variances. The Association of Public Health Laboratories (APHL), for example, has published its ''Guidance for State Medical Cannabis Testing Programs'' to help sort through the confusing tangle of existing testing laws, where they exist. They exemplify this variation of law in their document<ref name="APHLGuide16">{{cite web |url=https://www.aphl.org/aboutAPHL/publications/Documents/EH-Guide-State-Med-Cannabis-052016.pdf |format=PDF |title=Guidance for State Medical Cannabis Testing Programs |author=Association of Public Health Laboratories |pages=35 |date=May 2016 |accessdate=01 February 2017}}</ref>:
 
<blockquote>As with most programs in the United States, every state takes a different approach. For example as of January 2016, New Jersey’s Public Health & Environmental Laboratories only test cannabis plant material. Just across the Hudson, however, New York’s Public Health Laboratory will not be testing any plant material, only cannabis extracts. In addition, the New York Department of Health will provide an oversight role for commercial cannabis laboratories that are licensed by the federal Drug Enforcement Administration (DEA) and approved for testing cannabis products. On the other hand, New Jersey state government does all testing in-house for the medical cannabis program.</blockquote>
 
As such, unlike their federal counterpart, it's difficult to make broad generalizations about cannabis regulations and their enforcement in the states. It becomes even more difficult when examining states that don't have clear, well-considered regulations or strong enforcement powers. Cambron ''et al.'' emphasized this issue in regards to the supply side, saying: "States without clearly defined regulations for medical cannabis supply have fostered gray markets for cannabis whereby individuals without documented medical conditions are able to easily obtain medical cannabis authorizations. This scenario has created substantial challenges for law enforcement in multiple states."<ref name="CambronState16" />
 
Cole ''et al.'' argue that in the end, it will take pressure on the federal government "to set up policy guardrails to steer state regulatory systems" in a more unified and safe direction. Drugged driving, use by minors, interstate distribution, relation to crime and firearms, consumer safety, and advertising are all issues the government should be tackling towards that goal, they say. Not that states aren't addressing these regulatory concerns; they are, but not in consistent ways.<ref name="ColeAll16">{{cite web |url=http://www.thirdway.org/report/all-state-marijuana-laws-are-not-created-equal |title=All State Marijuana Laws Are Not Created Equal |work=Third Way |author=Cole, T.; Trumble, S.; Hatalsky, L.E. |date=17 February 20116 |accessdate=02 February 2017}}</ref>
 
===Standardization===
While federal, state, and local governments wrestle with the regulatory frameworks surrounding cannabis, scientists and government officials are carrying on, doing what they can to harmonize those regulations with emerging industry standards and guidelines. For example, state officials from Colorado, New Mexico, Oregon, and Washington teamed up to give a presentation called "State Regulatory Approaches to Cannabis Testing, Operations and Product Logistics" at the July 2016 Cannabis Quality, Strategies and Solutions Summit. That presentation focused on the harmonization of regulatory standards and frameworks across states, as well as discussions of what scientific efforts are required to support those standards and frameworks.<ref name="CQSSS16">{{cite web |url=http://chernislaw.com/news/wp-content/uploads/2016/07/NEW-Cannabis-Quality-Summit-Main-Summit-Agenda.pdf |format=PDF |title=Cannabis Quality, Strategies and Solutions Summit - Agenda |publisher=Information Forecast, Inc |date=July 2016 |accessdate=02 February 2017}}</ref> Additionally, organizations such as Americans for Safe Access Foundation (ASAF), American Herbal Pharmacopoeia (AHP), American Herbal Products Association (AHPA), and the American Oil Chemists' Society (AOCS) have been developing standards, methods, and certifications for analysis, extraction, labeling, and laboratory operations surrounding medical (and recreational) marijuana.<ref name="InfocastNew16">{{cite web |url=http://infocastinc.com/industries/new-certification-program-brings-quality-assurance-to-the-medical-marijuana-industry/ |title=New Certification Program Brings Quality Assurance to the Medical Marijuana Industry |publisher=Information Forecast, Inc |date=2016 |accessdate=02 February 2017}}</ref><ref name="AHPARecomm16">{{cite web |url=http://www.ahpa.org/Portals/0/pdfs/AHPA_Recommendations_for_Regulators_Cannabis_Operations.pdf |format=PDF |title=Recommendations for Regulators – Cannabis Operations |author=Cannabis Committee, AHPA |publisher=American Herbal Products Association |date=02 February 2016}}</ref><ref name="AHPCanna14">{{cite book |url=http://www.herbal-ahp.org/order_online.htm |title=''Cannabis Inflorescence'': ''Cannabis'' spp. |publisher=American Herbal Pharmacopoeia |editor=Upton, R.; Craker, L.; ElSohly, M. et al. |year=2014 |isbn=1929425333}}</ref><ref name="MarcuJahan16">{{cite web |url=https://www.projectcbd.org/article/jahan-marcu-cannabis-lab-testing-safety-protocols |title=Jahan Marcu: Cannabis Lab Testing & Safety Protocols |work=Project CBD |author=Project CBD; Marcu, J. |publisher=Project CBD |date=16 March 2016 |accessdate=03 February 2017}}</ref><ref name="CassidayTheHighs16">{{cite web |url=https://www.aocs.org/stay-informed/read-inform/featured-articles/the-highs-and-lows-of-cannabis-testing-october-2016 |title=The Highs and Lows of Cannabis Testing |author=Cassiday, L. |work=INFORM |publisher=American Oil Chemists' Society |date=October 2016 |accessdate=03 February 2017}}</ref> Notable among them is the Foundation of Cannabis Unified Standards (FOCUS), which has been working to produce internationally applicable voluntary consensus standards for various parts of the cannabis business chain, including cultivation, extraction, laboratory testing, and packaging. FOCUS completed its public review process and finalized its standards in July 2016<ref name="FOCUSPublic16">{{cite web |url=http://www.focusstandards.org/public-review-of-cannabis-standards/ |title=Public Review Completes Development Process |publisher=FOCUS |date=2016 |accessdate=16 February 2016}}</ref>, though it's not clear how to gain access to them.
 
 
==Laboratory testing of cannabis==
The overview mentioned how regulators, users, and industry are calling for improved standardization of the production and testing of medical and recreational marijuana. Without proper testing of medical and recreational marijuana, several issues are bound to arise<ref name="HazekampCanna12" /><ref name="BushWorlds15" /><ref name="RutschQuality15" /><ref name="KuzdzalACloser16" /><ref name="CassidayTheHighs16" /><ref name="CANORMLHow11">{{cite web |url=http://canorml.org/news/ringtest.html |title=How Accurate Is Cannabis Potency Testing? |publisher=California NORML |date=21 September 2011 |accessdate=03 February 2017}}</ref>:
 
* label claims may not match actual contents;
* contaminants may linger, causing illness or even death;
* chemical properties and medicinal benefits of specific strains and their unique cannabinoid-turpene profiles can't be isolated; and
* research on potential therapeutic qualities can't be replicated, hindering scientific progress.
 
In 2011 — a year before any U.S. state had enacted broad legalization of recreational marijuana — California NORML reported that its assessment of analytical cannabis testing laboratories' accuracy found that while California labs broadly reached +/- 20 percent consistency from a replicate sample, three out of ten provided unfavorable results on at least half of their tests. Similar wide-ranging discrepancies were also found among edibles, extracts, and tinctures, and NORML found that none of the labs could reach two decimal points precision of cannabinoid results despite laboratory claims stating otherwise.<ref name="CANORMLHow11" /> Another report out of Washington State in January 2015, not long after recreational marijuana sales to the public (requiring accredited lab testing prior) began<ref name="WLCBFAQ">{{cite web |url=http://lcb.wa.gov/mj2015/faqs_i-502 |title=FAQs on I-502 |publisher=Washington State Liquor and Cannabis Board |accessdate=03 February 2017}}</ref>, found blind tests of recreational marijuana at dispensaries could range as much as 7.5 percent in accuracy from its corresponding label.<ref name="BushWorlds15" /> Further issues in 2016 with alleged partiality by some Washington testing laboratories prompted emergency proficiency testing rules to be enacted.<ref name="YoungSome16">{{cite web |url=http://www.seattletimes.com/seattle-news/marijuana/some-pot-labs-in-state-failed-no-pot-at-all-says-scientist/ |title=Some pot labs in state failed no pot at all, says scientist |author=Young, B. |work=The Seattle Times |publisher=The Seattle Times Company |date=05 January 2016 |accessdate=03 February 2017}}</ref><ref name="Coughlin-BogueToCombat16">{{cite web |url=https://www.leafly.com/news/politics/to-combat-claims-of-inconsistency-washington-testing-labs-turn-to |title=To Combat Inconsistency, Washington Testing Labs Turn to Self-Policing |work=Leafly - Politics |author=Coughlin-Bogue, T. |publisher=Leafly Holdings, Inc |date=11 March 2016 |accessdate=03 February 2017}}</ref> (Proficiency testing essentially requires a laboratory in question to test a sample with known properties, and then those results are compared to those of a neutral third-party lab testing the same sample.)
 
These discrepancies and deficiencies highlight the growing need for homogenization of testing methods and procedures, if not nationally at least across an entire state. Such homogenization would, in theory, not only positively affect the quality of product but also provide greater consumer confidence that label and product match. As Marketing Director Scott Kuzdzal of Shimadzu pointed out during a January 2017 webinar on analytic testing of cannabis, poor sample preparation, lack of thorough testing, and the manual process itself — which can introduce user error, particularly when good laboratory practices aren’t used — all can contribute to discrepancies between label and product.<ref name="KuzdzalOpp17">{{cite web |url= http://www.shimadzu.com.cn/an/news-events/news/2017/4381.html |archiveurl=http://web.archive.org/web/20170119200158/http://www.shimadzu.com.cn/an/news-events/news/2017/4381.html |title=Webinar: Opportunities & Challenges in Cannabis Analytical Testing |author=Kuzdzal, S.A. |publisher=Shimadzu Corporation |date=19 January 2017 |archivedate=19 January 2017 |accessdate=03 February 2017 |quote=Source is actual webinar.}}</ref> When dispensaries, edible manufacturers, and supplement companies perform insufficient lab testing or overstate claims on labels, it reduces consumer confidence, and both state and federal authorities — including the FDA — have to interject.<ref name="YoungSome16" /><ref name="Coughlin-BogueToCombat16" /><ref name="FDAWarn16">{{cite web |url=http://www.fda.gov/newsevents/publichealthfocus/ucm484109.htm |title=2016 Warning Letters and Test Results for Cannabidiol-Related Products |work=Public Health Focus |publisher=U.S. Food and Drug Administration |date=31 August 2016 |accessdate=03 February 2017}}</ref>
 
As was mentioned at the end of the previous section on state regulation, efforts to improve testing methods and procedures, with the goal of seeing the best of them become standards, are ongoing. Where are those efforts now, and where are they going? Before we can examine that, we first need to briefly look at what aspects of cannabis are actually being analyzed.
 
===Analytical aspects of cannabis===
====Cannabinoids====
As of mid-2015, researchers have identified 104 of the more than 750 constituents of ''Cannabis sativa'' as cannabinoids<ref name="RadwanIso15" />, active chemical compounds that act in a similar way to compounds our body naturally produces. Many of our body's cells have cannabinoid receptors capable of modulating neurotransmitter release in the brain and other areas.<ref name="WHOTheHealth16" /> The plant's cannabinoids vary, with each bonding to specific receptors in our body, providing differing effects. From a theoretical and medical standpoint, crafting a strain of cannabis that has specific cannabinoids that can aid with a particular malady, while also carefully reproducing the grow conditions to consistently make that strain in the future, is a desirable but difficult goal to achieve.<ref name="RahnCannab14">{{cite web |url=https://www.leafly.com/news/cannabis-101/cannabinoids-101-what-makes-cannabis-medicine |title=Cannabinoids 101: What Makes Cannabis Medicine? |work=Leafly - Cannabis 101 |author=Rahn, B. |publisher=Leafly Holdings, Inc |date=22 January 2014 |accessdate=03 February 2017}}</ref>
 
Lab testing of cannabinoids is done primarily as a measure of psychoactive "potency," though cannabinoids have many other potential therapeutic uses. Current laboratory testing looks at only a handful of cannabinoids; more research and development of analytical techniques that can quickly and accurately detect and separate the the rest is required.<ref name="KuzdzalUnrav15" /> Some of the major cannabinoids tested for include<ref name="KuzdzalUnrav15" /><ref name="APHLGuide16" /><ref name="RahnUnder14">{{cite web |url=https://www.leafly.com/news/cannabis-101/understanding-cannabis-testing |title=Understanding Cannabis Testing: A Guide to Cannabinoids and Terpenes |work=Leafly - Cannabis 101 |author=Rahn, B. |publisher=Leafly Holdings, Inc |date=09 October 2014 |accessdate=03 February 2017}}</ref>:
 
* '''THC (∆9-Tetrahydrocannabinol)''': This is the most commonly known cannabinoid found in cannabis, notable for its strong psychoactive effects and ability to aid with pain, sleep, and appetite issues. Included is its analogue ∆8-Tetrahydrocannabinol (which shows notably less strong psychoactive effects than ∆9<ref name="NIHDelta8">{{cite web |url=https://www.cancer.gov/publications/dictionaries/cancer-drug?cdrid=485262 |title=delta-8-tetrahydrocannabinol |work=NCI Drug Dictionary |publisher=National Institutes of Health, National Cancer Institute |accessdate=08 February 2017}}</ref>) and its homologue THCV (Tetrahydrocannabivarin), which tends to appear in trace amounts and has a more pronounced psychoactive effect, but for a shorter duration. THCV shows promise in fighting anxiety, tremors from neurological disorders, appetite issues, and special cases of bone loss. Also notable is Δ9-THCA (Δ9-Tethrahydrocannibinolic acid), a non-psychoactive biosynthetic precursor to THC.
 
* '''CBC (Cannabichromene)''': This non-psychoactive cannabinoid is found in trace amounts; however, it tends to be markedly more effective at treating anxiety and stress than CBD (see next). It's also notable for its anti-inflamatory properties and potential use for bone deficiencies.
 
* '''CBD (Cannabidiol)''': CBD is a non-psychoactive component of cannabis, typically accounting for up to 35 to 40 percent of cannabis extracts. It acts as a counter-balance to THC, regulating its psychoactivity. It's been researched as a treatment for anxiety, sleep loss, inflammation, stress, pain, and epilepsy, among other afflictions. Included is its homologue CBDV (Cannabidivarin), which is also non-psychoactive and demonstrates promise as a treatment for epileptic seizures. Also notable is CBDA (Cannabidiolic acid), a non-psychoactive biosynthetic precursor to CBD.
 
* '''CBG (Cannabigerol)''': This cannabinoid is also non-psychoactive but only appears in trace amounts of cannabis. If has potential as a sleep aid, anti-bacterial, and cell growth stimulant. Also notable is CBGA (Cannabigerolic acid ), a non-psychoactive biosynthetic precursor to CBG.
 
* '''CBN (Cannabinol)''': CBN is mildly psychoactive at best and appears only in trace amounts in ''Cannabis sativa'' and ''Cannabis indica''. It occurs largely as a metabolite of THC and tends to have one of the strongest sedative effects among cannabinoids. It shows promise as a treatment for insomnia, glaucoma, and certain types of pain.
 
====Terpenes====
Mandated lab testing of terpenes — volatile organic compounds that distinctly affect cannabis aroma and taste — is done primarily as a way to ensure proper labeling of cannabis and related products, including extracts and concentrates, so buyers have confidence in what they are purchasing.<ref name="HabibTesting13">{{cite web |url=http://lcb.wa.gov/publications/Marijuana/BOTEC%20reports/1c-Testing-for-Psychoactive-Agents-Final.pdf |format=PDF |title=Testing for Psychoactive Agents |author=Habib, R.; Finighan, R.; Davenport, S. |publisher=BOTEC Analysis Corp |date=24 August 2013 |accessdate=08 February 2017}}</ref><ref name="CMTLabs">{{cite web |url=http://www.cmtlaboratory.com/test-services/tests-offered/ |title=Tests Offered |publisher=CMT Laboratories |accessdate=08 February 2017}}</ref><ref name="WercShop">{{cite web |url=http://thewercshop.com/services/terpene-profiling-services/ |title=Terpene Profiling Services |publisher=The Werc Shop |accessdate=08 February 2017}}</ref> However, additional lab research goes into terpenes as they also show potentially useful pharmacological properties<ref name="HabibTesting13" /><ref name="WercShop" /><ref name="AndreCannabis16">{{cite journal |title=''Cannabis sativa'': The plant of the thousand and one molecules |journal=Frontiers in Plant Medicine |author=Andre, C.M.; Hausman, J.-F.; Guerriero, G. |volume=7 |pages=19 |year=2016 |doi=10.3389/fpls.2016.00019 |pmid=26870049 |pmc=PMC4740396}}</ref>, and they demonstrate synergies (referred to at times as the "entourage effect") with cannabinoids that largely still require further exploration.<ref name="CassidayTheHighs16" /><ref name="AndreCannabis16" /><ref name="WercShop" /><ref name="WachsbergerTerpene16">{{cite web |url=http://www.bloomcityclub.com/terpine-testing-the-future-of-cannabis-is-here/ |title=Terpene Testing: The future of Cannabis is here |author=Wachsberger, K. |work=Bloom Blog |publisher=Bloom City Club |date=02 February 2016 |accessdate=08 February 2017}}</ref> Testing for specific terpenes (discussed later) is less of a standardized practice, though it's rapidly improving.<ref name="HabibTesting13" /> Commonly tested terpenes by third-party testing labs include<ref name="WercShop" /><ref name="CMTLabs" /><ref name="AndreCannabis16" /><ref name="CassidayTheHighs16" /><ref name="RahnUnder14" /><ref name="SCLabs">{{cite web |url=http://sclabs.com/terpene-analysis/ |title=Terpene Analysis |publisher=SC Labs, Inc |accessdate=08 February 2017}}</ref>:
 
* Bisabolol
* Caryophyllene
* Cymene
* Humulene
* Limonene
* Linalool
* Myrcene
* Phytol
* Pinene
* Terpinolene
 
====Contaminates====
Generally speaking, a contaminate is an unwanted substance that may show up in the final product, be it recreational marijuana or a pharmaceutical company's therapeutic tincture. The following are examples of contaminates that laboratories may test for in cannabis products.
 
'''Pesticides''': Pesticides represent the Wild West of not only growing cannabis but also performing analytical testing on it. One of the core issues, again, is the fact that on the federal level marijuana is illegal. Because it's illegal, government agencies such as the Environmental Protection Agency (EPA) don't test and create standards or guidelines for what's safe when it comes to residual pesticides, let alone how to best test for them.<ref name="BorelTheWild15">{{cite web |url=https://www.theatlantic.com/health/archive/2015/08/pot-marijuana-pesticide-legalization/401771/ |title=The Wild West of Marijuana Pesticides |author=Borel, B. |work=The Atlantic |publisher=The Atlantic Monthly Group |date=31 August 2015 |accessdate=09 February 2017}}</ref><ref name="ZhangNobody15">{{cite web |url=https://www.wired.com/2015/08/nobody-knows-pesticides-legal-marijuana// |title=Nobody Knows What to Do About Pesticides in Legal Marijuana |author=Zhang, S. |work=Wired |publisher=Condé Nast |date=07 August 2015 |accessdate=09 February 2017}}</ref> Additionally, researchers face their fair share of difficulties obtaining product to test. The end result is we don't know much about how inhalation of pesticide-coated marijuana smoke affects long-term health<ref name="BorelTheWild15" /><ref name="ZhangNobody15" />, and we don't have standards for pesticide application and testing.<ref name="CassidayTheHighs16" /> With numerous pesticide products and little oversight on what growers apply to their plants, combined with the technical difficulty of testing for pesticides in the lab, pesticides remain one of the most difficult contaminates to test for.<ref name="CassidayTheHighs16" /> That said, several classes of of pesticides are commonly applied during cannabis cultivation and can be tested for by labs<ref name="APHLGuide16" /><ref name="KuzdzalACloser16" /><ref name="FarrerTech15">{{cite web |url=https://public.health.oregon.gov/PreventionWellness/marijuana/Documents/oha-8964-technical-report-marijuana-contaminant-testing.pdf |format=PDF |title=Technical Report: Oregon Health Authority’s Process to Determine Which Types of Contaminants to Test for in Cannabis Products, and Levels for Action |author=Farrer, D.G. |publisher=Oregon Health Authority |date=December 2015 |accessdate=09 February 2017}}</ref>:
 
* '''avermectins''': used as insecticides that are useful against mites, which are a common problem for cultivators
* '''carbamates''': insecticide similar to organophosphates, but with decreased dermal toxicity and higher degradation
* '''organophosphates''': functions as the base of many insecticides and herbicides, valued for its easy organic bonding
* '''pyrethroids''': functions as the base of most household insecticides and exhibits insect repellent properties
 
 
'''Solvents''': In 2003, Canadian Rick Simpson published a recipe of sorts for preparing cannabis extract via the use of solvents such as naphtha or petroleum ether. Claiming the resulting oil helped cure his skin cancer, others hoping for a cure tried it, and the solvent method of preparation grew in popularity. Dubious healing claims aside, the solvent extraction method remains viable, though it has evolved over the years to include less harmful solvents such as supercritical carbon dioxide, which has low toxicity, low environmental impact, and beneficial extraction properties.<ref name="CassidayTheHighs16" /><ref name="RomanoCannabis13">{{cite journal |url=http://www.stcm.ch/en/files/hazekamp_cann-oil_2013.pdf |format=PDF |title=Cannabis oil: Chemical evaluation of an upcoming cannabis-based medicine |journal=Cannabinoids |author=Romano, L.L.; Hazekamp, A. |volume=1 |issue=1 |pages=1–11 |year=2013}}</ref><ref name="PeachSuper14">{{cite journal |title=Supercritical carbon dioxide: A solvent like no other |journal=Journal of Organic Chemistry |author=Peach, J.; Eastoe, J. |volume=10 |pages=1878-95 |year=2014 |doi=10.3762/bjoc.10.196 |pmid=25246947 |pmc=PMC4168859}}</ref> However, chemical solvents are still used, and if not evaporated out properly, the remaining solvents can be particularly harmful to sick patients using the extract. As for what solvents should be tested for, it gets a bit trickier, though Chapter 467 of ''United States Pharmacopeia and The National Formulary'', the Oregon Health Authority's December 2015 technical report on contaminant testing of cannabis, and the Massachusetts Department of Public Health's response to public comments on cannabis testing provide helpful guidance. Listed solvents include benzene, butane, cumene, dimethoxyethane, hexane, and pentane, among others.<ref name="KuzdzalACloser16" /><ref name="APHLGuide16" /><ref name="CassidayTheHighs16" /><ref name="FarrerTech15" /><ref name="USPNF467">{{cite web |url=https://www.usp.org/sites/default/files/usp_pdf/EN/USPNF/generalChapter467Current.pdf |format=PDF |title=<467> Residual Solvents |work=United States Pharmacopeia and The National Formulary |publisher=United States Pharmacopeial Convention |date=01 July 2007 |accessdate=09 February 2017}}</ref><ref name="MDPHResponse">{{cite web |url=http://www.mass.gov/eohhs/docs/dph/quality/medical-marijuana/lab-protocols/external-comment-response-020416-final.pdf |format=PDF |title=Response to Public Comments |author=Bureau of Healthcare Safety and Quality |publisher=Massachusetts Department of Public Health |date=12 February 2016 |accessdate=14 February 2017}}</ref>
 
 
'''Heavy metals''': 2013 research on contaminant testing on the behalf of Washington State provides insights into heavy metals and why they're looked for in cannabis testing. That research, as well as other sources, tell us<ref name="KuzdzalACloser16" /><ref name="APHLGuide16" /><ref name="CassidayTheHighs16" /><ref name="DaleyTesting13">{{cite web |url=http://lcb.wa.gov/publications/Marijuana/BOTEC%20reports/1a-Testing-for-Contaminants-Final-Revised.pdf |format=PDF |title=Testing ''Cannabis'' for Contaminants |author=Daley, P.; Lampach, D.; Sguerra, S. |publisher=BOTEC Analysis Corp |date=12 September 2013 |accessdate=09 February 2017}}</ref>:
 
* Heavy metals contribute to several health problems, including those of a neurological nature.
* Cannabis can "hyperaccumulate metals from contaminated soils."
* Research parallels can be found in tobacco research and how the FDA regulates heavy metal content in foods.
* The most prominently tested heavy metals include arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), mercury (Hg), and nickel (Ni).
 
 
'''Mycotoxins and microorganisms''': "The ideal conditions for cannabis growth are also ideal for the growth of potentially harmful bacteria and fungi, including yeast and molds," say Shimadzu's Scott Kuzdzal and William Lipps, "therefore microbial contamination poses health risks to consumers and immunocompromised individuals."<ref name="KuzdzalUnrav15" /> In truth, these concerns have already borne out; most recently the University of California, Davis reported in February 2017 one of its patients had contracted an incurable fungal infection from inhaling aerosolized marijuana. They later tested 20 marijuana samples from Northern California dispensaries — using specialized techniques — and found a wide variety of potentially hazardous microorganisms.<ref name="UCDavis17">{{cite web |url=http://www.ucdmc.ucdavis.edu/publish/news/newsroom/11791 |title=UC Davis study finds mold, bacterial contaminants in medical marijuana samples |publisher=UC Davis |date=07 February 2017 |accessdate=10 February 2017}}</ref>
 
The degree to which such contaminates commonly appear in grown and stored cannabis material and to which microbiological contaminates should be tested is not clear, however. As mentioned previously, neither the U.S. EPA or neighboring Health Canada provide any significant guidance on cannabis testing, including microbiological contaminates.<ref name="KennardYouAre16">{{cite web |url=https://tantaluslabs.com/populace/you-are-probably-smoking-mouldy-weed-why-does-quality-assurance-matter/ |title=You are Probably Smoking Mouldy Weed - Why Does Quality Assurance Matter? |work=Populace |author=Kennard, M. |publisher=Tantalus Labs |date=02 June 2016 |accessdate=10 February 2017}}</ref> Like heavy metal testing, parallels are drawn from microbial testing guidelines and standards relating to tobacco and food, where they exist.<ref name="KennardYouAre16" /> As warm, moist environments are conducive to microorganism growth, maintaining stable moisture levels during cultivation and storage is essential. Regularly measuring water activity — how moist something is — is particularly useful as a front-line preventative tool to better ensure microbial growth is limited.<ref name="FarrerTech15" /> Regardless, testing of some kind is still required by many U.S. states, including for organisms such as<ref name="KuzdzalUnrav15" /><ref name="APHLGuide16" /><ref name="CassidayTheHighs16" /><ref name="FarrerTech15" /><ref name="UCDavis17" /><ref name="KennardYouAre16" /><ref name="CANNAMicro14">{{cite web |url=http://www.fundacion-canna.es/en/microbiological-study-cannabis-samples |title=Microbiological study of Cannabis samples |publisher=Fundación CANNA |date=2014 |accessdate=10 February 2017}}</ref><ref name="RussoCann13">{{cite book |url=https://books.google.com/books?id=qH-2Lj9x7L4C&pg=PT457&lpg=PT457 |chapter=Chapter 30: Contaminants and Adulterants in Herbal Cannabis |title=Cannabis and Cannabinoids: Pharmacology, Toxicology, and Therapeutic Potential |author=McPartland, J.M. |editor=Russo, E.B. |publisher=Routledge |year=2013 |pages=478 |isbn=9781136614934}}</ref>:
 
* Aflatoxin
* ''Alternaria''
* ''Aspergillus''
* ''Cryptococcus''
* ''E. coli''
* ''Mucor''
* ''Penicillium''
* ''Salmonella''
 
===Methods and guidelines===
Now that we've addressed what's being tested for, we can move on to how they're being tested and what's being done to improve testing methods and procedures, including associated guidelines and recommendations. It would be beyond the scope of this guide to include every state's laws and guidelines on cannabis testing; entities such as [https://www.leafly.com/news/industry/leaflys-state-by-state-guide-to-cannabis-testing-regulations Leafly Holdings]<ref name="RoughLeaflysState">{{cite web |url=https://www.leafly.com/news/industry/leaflys-state-by-state-guide-to-cannabis-testing-regulations |title=Leafly’s State-by-State Guide to Medical Cannabis Testing Regulations |author=Rough, Lisa |work=Leafly - Industry |publisher=Leafly Holdings, Inc}}</ref> and [http://cannaguardsecurity.com/cannabiz-resources/ CannaGuard Security]<ref name="CSCanna16">{{cite web |url=http://cannaguardsecurity.com/cannabiz-resources/ |title=CannaBiz Resources |publisher=CannaGuard Security |date=2016 |accessdate=10 February 2017}}</ref> provide such online resources. Instead, this section will focus on current and promising techniques using generalizations based on information from multiple sources. If any guidelines and recommendations are known, they'll be included.
 
====Sampling====
Random, representative sampling is encouraged. When dealing with solid cannabis, BOTEC Analysis recommends a "quartering" method that divides the sample into four equal parts and takes portions from opposite sections of a square-shaped arrangement of the sample. For liquid cannabis products, remembering to stir before sample collection is advised.<ref name="APHLGuide16" /> When deriving a sample from a cannabis-laden edible, the QuEChERS approach used by food safety labs for pesticide testing has practical use.<ref name="RigdonExtract16">{{cite web |url=http://blog.restek.com/?p=25790 |title=Extraction Method for Cannabinoid Analysis in Edibles: Too Much of a Good Thing |author=Rigdon, A. |work=ChromaBLOGraphy |publisher=Restek Corporation |date=12 May 2016 |accessdate=16 February 2017}}</ref> In fact, a variety of parallels have been drawn from the food and herbal medicine industries' sampling guidelines, including from the Codex Alimentarius Commission's ''CAC/GL 50-2004 General Guidelines on Sampling'' as well as various chapters of the ''United States Pharmacopeia and The National Formulary''.<ref name="APHLGuide16" /><ref name="CACGL50-2004">{{cite web |url=http://www.fao.org/fao-who-codexalimentarius/sh-proxy/en/?lnk=1&url=https%253A%252F%252Fworkspace.fao.org%252Fsites%252Fcodex%252FStandards%252FCAC%2BGL%2B50-2004%252FCXG_050e.pdf |format=PDF |author=Codex Alimentarius Commission |title=CAC/GL 50-2004 General Guidelines on Sampling |pages=69 |accessdate=15 February 2017}}</ref> As the APHL points out, "[g]ood sampling is key to improving analytical data equivalency among organizations," and it provides a solid base for any future testing and standardization efforts.<ref name="APHLGuide16" />
 
Additional sampling insight can be found by examining other states' guidelines, e.g., Massachusetts' ''Protocol for Sampling and Analysis of Finished Medical Marijuana Products and Marijuana-Infused Products for Massachusetts Registered Medical Marijuana Dispensaries''.<ref name="DPHMassProto16">{{cite web |url=http://www.mass.gov/eohhs/docs/dph/quality/medical-marijuana/lab-protocols/finished-mmj/final-revised-mdph-mmj-mips-protocol.pdf |format=PDF |title=Protocol for Sampling and Analysis of Finished Medical Marijuana Products and Marijuana-Infused Products for Massachusetts Registered Medical Marijuana Dispensaries |author=Bureau of Health Care Safety and Quality |publisher=Massachusetts Department of Public Health |pages=25 |date=05 February 2016 |accessdate=17 February 2017}}</ref>
 
====Cannabinoid testing====
Quantifying cannabinoids for label accuracy is a major goal of testing, though calculation and testing processes may vary slightly from state to state. Despite any differences, laboratorians generally agree that when testing for cannabinoids such as THC and CBD, as well as their respective biosynthetic precursors THCA and CBDA, the methodology used must be scrutinized. The naturally occurring THCA of cannabis isn't psychoactive; it requires decarboxylation (a chemical reaction induced by drying/heating that releases carbon dioxide) to convert into the psychoactive cannabinoid THC. Chemical calculations show that the process of decarboxylation results in approximately 87.7 percent of the THCA's mass converting to THC, with the other 12.3 percent bubbling off as CO<sub>2</sub> gas.<ref name="CAWhy1">{{cite web |url=http://conflabs.com/why-0-877/ |title=Why 0.877? |publisher=Confidence Analytics |date=10 February 2016 |accessdate=16 February 2017}}</ref> The problem with this in the testing domain is gas chromatography (GC) involves heating the sample solution. If you, the lab technician, require precise numbers of both THCA and THC, then GC analysis poses the risk of under-reporting THC total values.<ref name="APHLGuide16" /> As such, liquid chromatography-diode array detection (LC-DAD) may be required if a concise profile of all cannabinoids must be made, primarily because it provides environmental stability for them all during analysis. If GC is used, the analysis requires extra considerations such as sample derivatization.<ref name="APHLGuide16" /><ref name="CassidayTheHighs16" /><ref name="RigdonAccurateJuly15">{{cite web |url=http://blog.restek.com/?p=14961 |title=Accurate Quantification of Cannabinoid Acids by GC – Is it Possible? |author=Rigdon, A. |work=ChromaBLOGraphy |publisher=Restek Corporation |date=29 July 2015 |accessdate=16 February 2017}}</ref><ref name="RigdonAccurateSept15">{{cite web |url=http://blog.restek.com/?p=15135 |title=Accurate Quantification of Cannabinoid Acids and Neutrals by GC – Derivatives without Calculus |author=Rigdon, A. |work=ChromaBLOGraphy |publisher=Restek Corporation |date=09 September 2015 |accessdate=16 February 2017}}</ref>
 
The APHL briefly describes analysis methods of cannabinoids using both LC and GC on pages 31–32 of their May 2016 ''Guidance for State Medical Cannabis Testing Programs''. They also point to New York Department of Health - Wadsworth Center's various guidance documents (MML-300, -301, and -303) for methodologies when testing sample types other than solids, particularly using high-performance liquid chromatography photodiode array detection (HPLC-PDA).<ref name="APHLGuide16" /><ref name="MML-300">{{cite web |url=https://www.wadsworth.org/sites/default/files/WebDoc/576578963/MML-300-01.pdf |format=PDF |title=Measurement of Phytocannabinoids using HPLC-PDA, NYS DOH MML-300 |author=Division of Environmental Health Sciences, Laboratory of Organic Analytical Chemistry |publisher=New York State Department of Health |pages=34 |date=03 November 2015 |accessdate=15 February 2017}}</ref> Overall, methods used in cannabinoid testing include<ref name="APHLGuide16" /><ref name="CassidayTheHighs16" /><ref name="MML-300" /><ref name="SCCann16">{{cite web |url=http://www.sigmaaldrich.com/content/dam/sigma-aldrich/docs/Sigma-Aldrich/General_Information/1/cannabis-testing.pdf |format=PDF |title=Cannabis Testing: Quality You Can Trust |publisher=Sigma-Aldritch Co. LLC |date=2016 |accessdate=15 February 2017}}</ref><ref name="AdamsNear16">{{cite web |url=https://www.cannabisindustryjournal.com/column/near-infrared-gc-and-hplc-applications-in-cannabis-testing/ |title=Near Infrared, GC and HPLC Applications in Cannabis Testing |author=Adams, T.; Bertone, M. |work=Cannabis Industry Journal |publisher=Innovative Publishing Co. LLC |date=30 November 2016 |accessdate=15 February 2017}}</ref>:
 
* Fourier transform infrared spectroscopy (FTIR; has limitations, such as requiring standard samples tested w/ other methods)
* Gas chromatography flame ionization detection (GC-FID; requires sample derivatization for both acid and neutral compounds; good with standards like 5α-cholestane, docosane, and tetracosane)
* Gas chromatography mass spectrometry (GC-MS; requires sample derivatization for both acid and neutral compounds; good with standards like deuterated cannabinoids)
* High-performance liquid chromatography photodiode array detection (HPLC-PDA; stable for all forms of cannabinoids)
* High-performance liquid chromatography UV detection (HPLC-UV)
* Supercritical fluid chromatography (SFC; newer technology w/ added benefits)
* Thin-layer chromatography (TLC; older, less common technology)
* Ultra-performance chromatography (UPC; newer technology w/ added benefits)
 
====Terpene testing====
Identifying and quantifying terpenes is one of the more difficult tasks facing laboratorians<ref name="CassidayTheHighs16" />:
 
<blockquote>Terpenes present an analytical challenge because they are nonpolar and structurally similar, and many structural isomers exist. Mass spectrometry (MS) cannot distinguish terpenes that co-elute from a GC column because many have the same molecular weight and share fragment ions.</blockquote>
 
Of course, types of gas chromatography work; but like cannabinoids, terpenes can degrade with the high heat of gas chromatography.<ref name="AdamsNear16" /> Combined with the problems mentioned above, highly specialized gas chromatography processes that include additional steps — such as full evaporation technique headspace gas chromatography flame ionization detection (FET-HS-GC-FID) — can be used to produce cleaner results, particularly for volatile components.<ref name="CassidayTheHighs16" /> It's less clear if high-performance liquid chromatography (HPLC) is used frequently; some entities such as Eurofins Experchem Laboratories claim HPLC works best for them<ref name="AdamsNear16" />, while others such as Restek Corporation claim the method is problematic at best.<ref name="HerringCanHP16">{{cite web |url=http://blog.restek.com/?p=33071 |title=Can HPLC-UV Be Used For Terpenes Analysis In Cannabis? |author=Herring, T. |work=ChromaBLOGraphy |publisher=Restek Corporation |date=29 December 2016 |accessdate=15 February 2017}}</ref>
 
Overall, methods for terpene identification and analysis include<ref name="CassidayTheHighs16" /><ref name="SCLabs" /><ref name="SCCann16" /><ref name="AdamsNear16" /><ref name="ShimadzuCLTS">{{cite web |url=https://www.ssi.shimadzu.com/products/literature/life_science/shimadzu_cannabis_brochure.pdf |format=PDF |title=Cannabis Testing Laboratory Solutions |publisher=Shimadzu Corporation |accessdate=14 February 2017}}</ref>:
 
* Full evaporation technique headspace gas chromatography flame ionization detection (FET-HS-GC-FID; tends to be semi-quantitative)
* Gas chromatography flame ionization detection (GC-FID)
* Gas chromatography mass spectrometry (GC-MS)
* Headspace gas chromatography mass spectrometry (HS-GC-MS)
* High-performance liquid chromatography (HPLC; may have limitations due to coelution of terpenes and cannabinoids at certain ranges<ref name="HerringCanHP16" />)
 
====Contaminate testing====
'''Pesticides''': Gas and liquid chromatography methods are by and large the staple of testing methods for pesticides, which remain "the hardest analyses that are going to be done in the cannabis industry."<ref name="CassidayTheHighs16" /> Notably, high-performance liquid chromatography tandem-mass spectrometry (HPLC-MS/MS) tends to be one of the most thorough methods says Emerald Scientific's CTO Amanda Rigdon. "Ninety-five percent of the pesticides out there can be analyzed by HPLC-MS/MS, although there are some that you would need a GC-MS/MS for," she says.<ref name="CassidayTheHighs16" /> Testing methods that have been used include<ref name="APHLGuide16" /><ref name="ShimadzuCLTS" />:
 
* Gas chromatography electron capture detection (GC-ECD)
* Gas chromatography mass spectrometry (GC-MS)
* Gas chromatography tandem-mass spectrometry (GC-MS/MS)
* Liquid chromatography mass spectrometry (LC-MS; also high-performance or HPLC-MS)
* Liquid chromatography tandem-mass spectrometry (LC-MS/MS; also high-performance or HPLC-MS/MS)
 
For quantification of pesticides in cannabis, the EPA's Residue Analytical Methods (RAM) or FDA's Pesticide Analytical Manual (PAM) provide guidance to labs.<ref name="APHLGuide16" /><ref name="FDAAnalysisofFoods11">{{cite web |url=https://archive.epa.gov/pesticides/methods/rammethods/web/html/ram12b.html |title=Residue Analytical Methods (RAM) |publisher=United States Environmental Protection Agency |date=20 February 2016 |accessdate=14 February 2017}}</ref><ref name="FDA_PAM">{{cite web |url=http://www.fda.gov/Food/FoodScienceResearch/LaboratoryMethods/ucm2006955.htm |title=Pesticide Analytical Manual (PAM) |publisher=United States Food and Drug Administration |date=07 June 2015 |accessdate=14 February 2017}}</ref>
 
 
'''Solvents''': Testing for solvents is largely standardized into a couple of options, which have parallels to existing pharmaceutical testing standards outlined in Chapter 467 of ''United States Pharmacopeia and The National Formulary'' (USP <467>)<ref name="USPNF467" /><ref name="APHLGuide16" /><ref name="CassidayTheHighs16" /><ref name="ShimadzuCLTS" />:
 
* Headspace gas chromatography/mass spectrometry (HS-GC/MS)
* Full evaporation technique headspace gas chromatography flame ionization detection (FET-HS-GC-FID)
 
Massachusetts and Oregon — and likely other states — have used a variety of guidance documents such as USP <467>, reports from the Commission of the European Communities' Scientific Committee on Food (now the European Food Safety Authority), and the International Conference on Harmonization's (ICH) Q3C(R5)<ref name="APHLGuide16" /><ref name="MDPHResponse" /><ref name="FarrerTech15" /> to set their action level testing values for particular solvents.
 
 
'''Heavy metals''': The methods used for quantifying levels of highly toxic metals in plants depend on ease-of-use, level of accuracy, and overall cost. The following methods are most common for testing cannabis and other plants<ref name="KuzdzalACloser16" /><ref name="APHLGuide16" /><ref name="CassidayTheHighs16" /><ref name="DavisAnalysis15">{{cite web |url=http://www.ssi.shimadzu.com/products/literature/aas/ssi-icp-002.pdf |format=PDF |title=Analysis of "The Big Four" Heavy Metals in Cannabis by USN-ICP-OES |author=Davis, D.; Long, K.; Masone, J.; Firmin, P. |publisher=Shimadzu Corporation |date=August 2015 |accessdate=14 February 2017}}</ref><ref name="ShimadzuCLTS" />:
 
* Inductively coupled plasma atomic emission spectroscopy (ICP-AES), sometimes called inductively coupled plasma optical emission spectrometry (ICP-OES) (at times coupled with an ultrasonic nebulizer)
* Inductively coupled plasma mass spectroscopy (ICP-MS)
* Inductively coupled plasma tandem-mass spectroscopy (ICP-MS/MS)
 
For quantification of metals in cannabis, the U.S. FDA's ICP-MS methodology document is often used.<ref name="APHLGuide16" /><ref name="FDAAnalysisofFoods11">{{cite web |url=http://www.fda.gov/downloads/Food/FoodborneIllnessContaminants/Metals/UCM272693.pdf |format=PDF |title=Analysis of Foods for As, Cd, Cr, Hg and Pb by Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) |publisher=United States Food and Drug Administration, Center for Food Safety and Applied Nutrition |date=25 April 2011 |accessdate=14 February 2017}}</ref>
 
 
'''Mycotoxins and microorganisms''': A standard method of testing for the existence of microorganisms is through the process of culturing a sample in a Petri dish, a common diagnostic method in microbiology. Enzyme-linked immunosorbent assay (ELISA) is also used, particularly to identify mycotoxins. However, Petri culture analysis isn't rigorous, and ELISA can be time consuming, as it's limited to one mycotoxin per test.<ref name="KuzdzalACloser16" /><ref name="CassidayTheHighs16" /><ref name="KennardYouAre16" /> The following are other, more precise techniques that are improving laboratorians' analyses, particularly using DNA snippets of microbiological contaminates<ref name="KuzdzalACloser16" /><ref name="CassidayTheHighs16" /><ref name="KennardYouAre16" /><ref name="ThompsonAMicro16">{{cite journal |title=A microbiome assessment of medical marijuana |journal=Clinical Microbiology and Infection |author=Thompson III, G.R.; Tuscano, J.M.; Dennis, M. et al. |pages=S1198-743X(16)30605-X |year=2017 |doi=10.1016/j.cmi.2016.12.001 |pmid=27956269}}</ref>:
 
* Quantitative polymerase chain reaction (qPCR)
* Whole metagenome shotgun (WMGS) sequencing
* Matrix-assisted laser desorption/ionization (MALDI)
* High-performance liquid chromatography (HPLC)
* Liquid chromatography tandem-mass spectrometry (LC-MS/MS)
 
The extent of mycotoxin testing required remains in question by several entities. The Association of Public Health Laboratories (APHL) claims "[t]here is no readily available evidence to support the contention that cannabis harbors significant levels of mycotoxins."<ref name="APHLGuide16" /> The Oregon Health Authority takes a more middle-ground approach, noting that testing for ''E. coli'' and ''Salmonella'' will "protect public health," though ''Aspergillus'' only deserves a warning for people with suppressed immune systems due to its prevalence in the environment.<ref name="FarrerTech15" /> USP <561> recommendations largely limit mycotoxin testing of botanical products to those borne from root or rhizome material<ref name="USPNF561">{{cite web |url=https://hmc.usp.org/sites/default/files/documents/HMC/GCs-Pdfs/c561.pdf |format=PDF |title=<561> Articles of Botanical Origin |work=United States Pharmacopeia and The National Formulary |publisher=United States Pharmacopeial Convention |date=01 July 2007 |accessdate=15 February 2017}}</ref>, "which THC-containing cannabis products presumably do not possess," emphasizes the APHL.<ref name="APHLGuide16" /> Regardless, U.S. Pharmacopeia's Chapter 561 remains a useful document for testing guidelines and limits regarding microbials.<ref name="USPNF561" /><ref name="APHLGuide16" /> In the less common case of dealing with powdered cannabis — a relatively new THC extract form — Chapter 2023 provides at least some testing parallels, though Dr. Tony Cundell, a microbiologist consulting for the pharmaceutical industry, suggests USP <2023> doesn't go far enough for immunocompromised patients.<ref name="CundellMicro15">{{cite web |url=http://www.americanpharmaceuticalreview.com/Featured-Articles/177487-Microbiological-Attributes-of-Powdered-Cannabis/ |title=Microbiological attributes of powdered cannabis |work=American Pharmaceutical Review |author=Cundell, T. |publisher=CompareNetworks, Inc |date=31 July 2015 |accessdate=15 February 2017}}</ref>
 
Somewhat related and worth mentioning is moisture content testing. As previously mentioned, warm, moist environments are conducive to microorganism growth, and regularly measuring water activity is useful for the prevention of microbial growth.<ref name="FarrerTech15" /> The APHL references specifications from the Dutch Office of Medical Cannabis that recommend water content be between five to ten percent in cannabis.<ref name="APHLGuide16" />
 
===Reports===
There's little in the way of standardization for lab reporting of cannabis test results, though some U.S. states have outlined requirements for what must be included in such reports. The Oregon Health Authority's ''Oregon Administrative Rules, Chapter 333, Division 64, Section 0100: Marijuana Item Sampling Procedures and Testing'' stipulates that any report must include total THC and total CBD (by dry weight) and, if discovered, "up to five tentatively identified compounds (TICS) that have the greatest apparent concentration." It also lays out requirements for pesticides, failed tests, limits of quantification, and specimen identifiers such as test batch number.<ref name="OHA333-064-0100">{{cite web |url=http://arcweb.sos.state.or.us/pages/rules/oars_300/oar_333/333_064.html |title=Oregon Health Authority, Public Health Division, Division 64, Accreditation of Laboratories |work=Oregon Administrative Rules |publisher=Oregon Secretary of State |accessdate=16 February 2017}}</ref> In late January 2017, Pennsylvania released its temporary regulations in support of its new medical marijuana program (28 Pa. Code Chapter 1171), which includes a section on test results and reporting (1171.31). The regulations stipulate reporting by electronic tracking system, which includes certificates of analysis with lot/batch number and the specific compounds and contaminates.<ref name="28PA1171">{{cite journal |url=http://www.health.pa.gov/My%20Health/Diseases%20and%20Conditions/M-P/MedicalMarijuana/Documents/Chapter%201171%20Laboratories%201-26-17.pdf |format=PDF |title=Title 28 - Health and Safety, Department of Health - 28 Pa. Code Ch. 1171 |journal=Pennsylvania Bulletin |author=Pennsylvania Department of Health |volume=46 |issue=52 |pages=8036–8041 |date=24 December 2016}}</ref> Regulations aside, it's largely up to the laboratory — and often by extension, the software they're using — to decide how a report is formatted. Some labs like Seattle-based Analytical 360 offer clean, color-based certificates of analysis, with high-magnification photographs, the chromatogram, potency, cannabinoid content, contaminate content, and explanation of limits, with the name of the approving analyst.<ref name="CC_A360">{{cite web |url=http://cannabis-chronicles.com/wp-content/uploads/Godzilla.pdf |format=PDF |title=Certificate of Analysis - Sample: Godzilla |publisher=Cannabis Chronicles |date=07 July 2014 |accessdate=16 February 2017}}</ref><ref name="C360Results">{{cite web |url=http://analytical360.com/testresults |title=Current Test Results |publisher=Analytical 360, LLC |accessdate=16 February 2017}}</ref> Others may simply generate a computer printout with the basic data and legend.<ref name="HydrioCan16">{{cite web |url=http://beyondchronic.com/question/can-you-help-me-analyze-lab-reports-of-cannabis-oil/ |title=Can you help me analyze lab reports of cannabis oil? |author=Hydrio |work=Beyond Chronic: Ask Old Hippie |date=August 2016 |accessdate=16 February 2017}}</ref> Reports may originate from the measuring device itself (e.g., an integrator in a chromatography device), middleware or data station attached to the instrument, or a laboratory information management system that accepted data from the instrument.<ref name="McKennaSetting15">{{cite web |url=http://www.slideshare.net/GenTechScientific/ica2015-cannabis-presentation |title=Setting Up Your Cannabis Lab for Potency Testing |author=McKenna, M. |work=SlideShare |publisher=GenTech |date=18 June 2015 |accessdate=16 February 2017}}</ref>
 
Though not directly related to laboratory testing, it's worth noting states also have their own reporting requirements for growers, processors, and dispensaries. Both Oregon and Washington, for example, require monthly reports related to medical marijuana transfers.<ref name="OMMPReporting">{{cite web |url=https://public.health.oregon.gov/DiseasesConditions/ChronicDisease/MedicalMarijuanaProgram/Pages/reporting.aspx |title=Reporting Requirements for Tracking Medical Marijuana |author=Public Health Division |publisher=Oregon Health Authority |accessdate=16 February 2017}}</ref><ref name="314-55 WAC">{{cite web |url=http://apps.leg.wa.gov/wac/default.aspx?cite=314-55&full=true |title=Chapter 314-55 WAC: Marijuana Licenses, Application Process, Requirements, and Reporting |work=Washington Administrative Code |publisher=Washington State Legislature |date=16 November 2016 |accessdate=16 February 2017}}</ref>
 
===Lab equipment===
As indicated in previous sections, spectrometry and chromatography have played and will continue to play an important role in cannabis laboratory testing. This should not be surprising: "mass spectrometry is superior to other spectral techniques in such features as sensitivity, selectivity, generation possibility of molecular mass/formula, and combinability with chromatography."<ref name="MilmanTech10">{{cite book |chapter=Chapter 2: Techniques and Methods of Identification |title=Chemical Identification and its Quality Assurance |author=Milman, B.L. |publisher=Springer Berlin Heidelberg |year=2010 |pages=23–39 |isbn=9783642153617 |doi=10.1007/978-3-642-15361-7_2}}</ref> Analyzing complex chemical compounds that have many features and which are at times difficult to differentiate from each other proves challenging, but these technologies excel in meeting that task.<ref name="MilmanTech10" /> Refer to the previous "Methods and guidelines" section to note the specific technology associated with each molecule and contaminate. Aside from spectrometry and chomatography equipment, the analysis of microorganisms in cannabis may turn to DNA analysis methods that require additional equipment such as a thermal cycler (qPCR) or sequencer (WMGS), or ELISA, which utilizes a photometer or spectrophotometer. Of course, preparing and storing samples requires equipment as well, such as microplates, centrifuges, comparison standards, capillaries, chemicals, columns, Petri dishes, scales, and disposable gloves. Software-based data management systems may also constitute as equipment and are discussed in the next section.
 
When it comes to purchasing lab equipment specifically for cannabis testing, a 2015 interview with Emerald Scientific's CTO Amanda Rigdon (then with Restek Corporation) provides good advice<ref name="TaylorCanna15">{{cite web |url=http://www.laboratoryequipment.com/article/2015/06/cannabis-testing-opens-whole-new-market |title=Cannabis Testing Opens Up a Whole New Market |author=Taylor, M. |work=Laboratory Equipment |publisher=Advantage Business Media |date=23 June 2015 |accessdate=17 February 2017}}</ref>:
 
* Industry-specific instrumentation isn't needed in most cases as most of the techniques and equipment used in food and herbal medicine testing have strong parallels to cannabis testing.
* That said, some sample preparation tools, standards, and consumables specifically marketed to the industry may very well make the job quicker and more reliable.
* Appropriate sample preparation techniques are just as vital as the equipment you use.
* Do your research; many instrument companies are examining methodologies usable on conventional equipment, lessening the need for more expensive devices.
* If buying used equipment, make sure the original manufacturer is still in-business and producing consumables and replacement parts. Make sure your planned methods match the equipment, and make sure it's not so old that it can't be serviced by a qualified technician.
 
===Software===
Laboratories increasingly depend on software to analyze, store, and share critical data from instruments and experiments.<ref name="VI11">{{cite web |url=http://www.virtualinformatics.com/content/Laboratory_informatics.htm |archiveurl=http://web.archive.org/web/20150425070143/http://virtualinformatics.com/content/Laboratory_informatics.htm |title=Laboratory Informatics |publisher=virtualinformatics.com |date=09 April 2011 |archivedate=25 April 2015 |accessdate=17 February 2017}}</ref> This has led to the development of laboratory-specific software like the [[laboratory information management system]] (LIMS), [[electronic laboratory notebook]] (ELN), and [[chromatography data management system]] (CDMS; sometimes simply CDS). These and other software systems such as "seed-to-sale" programs can also play an important role in the cannabis testing laboratory.
 
====LIMS====
Laboratories of all types use LIMS software to manage the wide variety of data, testing and analysis workflows, and other enterprise activities typical of them. This generally includes — but is not limited to — sample reception, workflow management, sample tracking and analysis, quality control, instrument data management, data storage, reporting, and document management.<ref name="SkobelevLab11">{{cite journal |title=Laboratory information management systems in the work of the analytic laboratory |journal=Measurement Techniques |author=Skobelev, D.O.; Zaytseva, T.M.; Kozlov, A.D. et al. |volume=53 |issue=10 |pages=1182–1189 |year=2011 |doi=10.1007/s11018-011-9638-7}}</ref> The cannabis testing laboratory is no exception, though its activities differ slightly from, for example, a clinical pathology laboratory. As such, a few additional features outside of what's typically found in a generic LIMS are required.
 
Features that may be incorporated into a cannabis testing LIMS that you might not necessarily find in an all-purpose LIMS include<ref name="BirosUsing15">{{cite web |url=https://www.cannabisindustryjournal.com/feature_article/using-lims-in-cannabis-laboratories/ |title=Using LIMS in Cannabis Laboratories |author=Biros, A.G. |work=Cannabis Industry Journal |publisher=Innovative Publishing Co. LLC |date=23 October 2015 |accessdate=21 February 2017}}</ref><ref name="PromiumELIMSCanna">{{cite web |url=https://www.promium.com/main/element-lims-cannabis |title=LIMS for Managing Cannabis Testing |publisher=Promium, LLC |accessdate=21 February 2017}}</ref><ref name="PharmWareHome">{{cite web |url=http://www.pharmware.net/ |title=PharmWare |publisher=PharmLabs LLC |accessdate=21 February 2017}}</ref><ref name="LLLIMSCanna">{{cite web |url=http://www.lablynxpress.com/index.php?title=LabLynx_LIMS_-_Cannabis |title=LabLynx LIMS - Cannabis |publisher=LabLynx, Inc |date=20 January 2017 |accessdate=21 February 2017}}</ref>:
 
* sample loading screens optimized for the industry, including differentiation between medical and recreational marijuana
* pre-loaded compliant test protocols, labels, and reports optimized and readily adjustable for a rapidly changing industry
* tools for creating new, compliant test protocols, labels, and reports
* a web API to integrate with state-required compliance reporting systems
* chain-of-custody (CoC) tracking, when necessary
* support for inventory reconciliation
 
As previously discussed, industry-specific test protocols largely focus on cannabinoids, terpenes, and a wide variety of contaminates, including excess water. However, as regulations continue to be in a state of flux and not particularly standardized, most LIMS developers are including the ability for users to adjust their protocols and even add new ones. And while CoC functionality is not entirely foreign to generic LIMS, it's particularly important in an industry where currently transporting even a cannabis test sample across state lines can create huge problems.
 
In cases where daily sample processing is infrequent and only a couple of chromatography machines are used, laboratories may weigh a decision between a LIMS and a chromatography-specific CDMS.
 
====CDMS====
Scientists on the research side of cannabis are certainly using CDMSs from Agilent, Thermo Scientific, Waters, and other to manage the data coming out of their chromatography equipment<ref name="PurschkeDevelop16">{{cite journal |title=Development and validation of an automated liquid-liquid extraction GC/MS method for the determination of THC, 11-OH-THC, and free THC-carboxylic acid (THC-COOH) from blood serum |journal=Analytical and Bioanalytical Chemistry |author=Purschke, K.; Heinl, S.; Lerch, O. et al. |volume=408 |issue=16 |pages=4379-88 |year=2016 |doi=10.1007/s00216-016-9537-5 |pmid=27116418 |pmc=PMC4875941}}</ref><ref name="AlbertiniDeterm15">{{cite web |url=http://tools.thermofisher.com/content/sfs/posters/PO-GC-MS-THC-Metabolites-Triple-Quad-EN.pdf |format=PDF |title=Determination of Tetrahydrocannabinol (THC) and Its Main Metabolites Using GC Triple Quadrupole Mass Spectrometry |author=Albertini, T.; Caruso, A. |publisher=Thermo Fisher Scientific |date=2015 |accessdate=17 February 2017}}</ref><ref name="LaytonAnalysis17">{{cite web |url=https://www.perkinelmer.com/lab-solutions/resources/docs/APP_Analysis-of-Cannabinoids-in-Hemp-Seed-Oils-by-HPLC-012317_01.pdf |format=PDF |title=Analysis of Cannabinoids in Hemp Seed Oils by HPLC Using PDA Detection |author=Layton, C.; Reuter, W.M. |publisher=PerkinElmer, Inc |date=23 January 2017 |accessdate=17 February 2017}}</ref>, and slowly but surely some of those CDMSs are beginning to also support spectrometer data management in a similar way.<ref name="DaviesCentral16">{{cite web |url=http://www.spectroscopyeurope.com/articles/tony-davies-column/3676-central-spectroscopic-data-systems-why-are-chromatographers-so-much-better-equipped |title=Central spectroscopic data systems: Why are chromatographers so much better equipped? |author=Davies, A.N. |work=Spectroscopy Europe |publisher=John Wiley & Sons Ltd; IM Publications LLP |date=12 April 2016 |accessdate=17 February 2017}}</ref> Additionally, some chromatography system developers will collaborate with CDMS vendors to develop software drivers — code that essentially acts as a translator between a device and a program — so chromatography devices can interact fully with the CDMS.<ref name="ShimadzuWaters">{{cite web |url=http://www.ssi.shimadzu.com/products/product.cfm?product=gcdriver |title=Waters Empower Shimadzu GC Driver Version 2 |publisher=Shimadzu Scientific Instruments |accessdate=17 February 2017}}</ref>
 
The CDMS likely has a place in the cannabis testing lab as well, though it may depend on the lab's data management needs and goals. In more complex labs with multiple instruments and significant daily processing workflows, a LIMS may make more practical sense.
 
====Seed-to-sale====
The use of seed-to-sale software is an emerging trend that is only tangentially related to laboratory testing of cannabis. Rather than testing laboratories, seed-to-sale software is found at cultivation sites, production facilities, and dispensaries, and that software is typically designed to be able to integrate with testing laboratory or other software. The goal: create a complete record of transaction, from the grown plant to the lab, producer, and seller. This sort of tracking is mandated in various ways by many U.S. states with legalization laws. "It’s there to prevent the diversion of marijuana, which the federal government still lists as a Schedule I substance, the most dangerous class of drugs," wrote Daniel Rothberg of the ''Las Vegas Sun'' in December 2015. "Tracking also ensures product safety, assists with audits and helps facilitate recalls."<ref name="RothbergSoft15">{{cite web |url=http://www.govtech.com/state/Software-Firms-Benefit-from-Governments-Seed-to-Sale-Marijuana-Tracking.html |title=Software Firms Benefit from Government’s Seed-to-Sale Marijuana Tracking |author=Rothberg, D. |work=Government Technology |publisher=e.Republic, Inc |date=29 December 2015 |accessdate=21 February 2017}}</ref> This type of software is able to track plant yields, attempted theft or diversion, patient preferences, extraction methods, batch weights, and various financial statistics for analysis.<ref name="SmythTrack16">{{cite web |url=https://mjfreeway.com/blog/track-and-trace-vs-seed-to-sale |title=Track and Trace vs. Seed to Sale |author=Smyth, H. |work=MJ Freeway Blog |publisher=MJ Freeway, LLC |date=21 June 2016 |accessdate=21 February 2017}}</ref><ref name="RothbergSoft15" />
 
===Testing labs and pricing info===
 
The following are known active cannabis testing labs, sourced from ''Marijuana Business Daily''<ref name="MBDTestingLabs">{{cite web |url=http://mjbizdaily.com/industry-directory/testing-lab-services/ |title=Marijuana Testing & Lab Services |work=Marijuana Business Daily |publisher=Anne Holland Ventures, Inc |accessdate=22 February 2017}}</ref>, ''Ganajapreneur''<ref name="GPTestingLabs">{{cite web |url=https://www.ganjapreneur.com/cannabis-testing-labs/ |title=Cannabis Testing Labs |work=Ganjapreneuer |accessdate=22 February 2017}}</ref>, and general internet searching:
 
====Serving multiple states====
 
* [http://www.actlaboratoriesinc.com/ ACT Laboratories] (IL, MI; pricing not public)
* [http://csalabs.com/ Cannasafe Analytics] (CA, NV, WA)
* [http://steephill.com/ Steep Hill] (AK, CA, HI, MD, NM, WA; pricing not public)
* [http://www.eviolabs.com/ EVIO Labs] (CA, OR; pricing not public)
 
 
====Serving individual states====
'''Alaska'''
 
* [http://www.canntest.com/ Canntest]] ($70–$230, single and package tests)
 
 
'''Arizona''':
 
* [http://www.arizonaanalytical.com/ Arizona Analytical] (Pricing not public)
* [http://desertvalleytesting.com/ Desert Valley Testing] ($15–$360, single and package tests)
 
 
'''California''':
 
* [http://anresco.com/ Anresco Laboratories] (Pricing not public)
* [http://budgenius.com Bud Genius] ($50/sample)
* [https://www.cannalysislabs.com/ Cannalysis] (Pricing not public)
* [https://coastalanalytical.com/ Coastal Analytical] ($77–$249, single and package tests)
* [http://www.cblabstesting.com/ CS Labs] (Pricing not public)
* [http://www.cwanalytical.com/ CW Analytical] (Pricing not public)
* [https://www.flowertesting.com/ Flower Potency Testing] ($65–80/test type)
* [http://greenstyleconsulting.com/ Green Style Analytics] ($55–$295, single and package tests)
* [http://www.harrenslab.com/ Harrens Lab] (Pricing not public)
* [http://www.sdpharmlabs.com PharmLabs] ($40–$569, single and package tests)
* [http://pureanalytics.net/ Pure Analytics] (Pricing not public)
* [http://sclabs.com SC Labs] (Pricing not public)
* [https://www.sequoia-labs.com Sequoia Analytical Labs] (Pricing not public)
* [http://thewercshop.com The Werc Shop] (Pricing not public)
 
 
'''Colorado''':
 
* [http://agricorlabs.com/ Agricor Laboratories] (Pricing not public)
* [https://www.aurum-labs.com/ Aurum Labs] (Pricing not public)
* [http://bonafideslab.com/ Bonafides Laboratory] (Pricing not public)
* [http://www.cmtlaboratory.com/ CMT Laboratories] ($15–$120, depending on test type)
* [http://gobianalytical.net/ Gobi Labs] ($30–$120, depending on test type)
* [http://greenhilllab.com/ GreenHill Laboratories] ($45–$120, depending on test type)
* [http://www.nordicanalytic.com Nordic Analytical Laboratories] (Pricing not public)
* [http://phytatech.com/ PhytaTech CO] (Pricing not public)
* [http://www.rm3.us/ Rm3 Labs] ($35–$237, single and package tests)
* [http://www.teqanalyticallabs.com/ TEQ Analytical Laboratories] (Pricing not public)
 
 
'''Connecticut''':
 
 
'''District of Columbia''':
 
* Steep Hill plans on expanding to here.
 
 
'''Florida''':
 
* [http://www.moderncanna.com/ Modern Canna Science (MCS)] (Pricing not public)
 
 
'''Hawaii''':
 
* [http://www.hitestmedicallabs.com/ HI Test Medical Labs] (Pricing not public)
* [http://www.hipharmlabs.com/ PharmLabs Hawaii] ($50–$775, single and package tests)
* [http://steephill.com/hawaii Steep Hill Hawaii] (Pricing not public)
 
 
'''Maine''':
 
* [http://www.testedlabs.com/ Tested Labs] ($25–35/sample)
 
 
'''Michigan''':
 
* [http://www.truenorthlaboratory.com/ True North Laboratory] ($30–$120, depending on test type)
 
 
'''Oregon'''
 
* [http://cascadia-labs.com/ Cascadia Labs] (Pricing not public)
* [https://chemhistory.com/ ChemHistory] (Pricing not public)
* [http://www.greenleaflab.org/ Green Leaf Lab] ($50–$355, single and package tests)
* [https://www.ironlaboratories.com/ Iron Laboratories] ($40–430, single and package tests w/o membership; cheaper with)
* [http://www.juniperanalyticsllc.com/ Juniper Analytics] ($50–$325, single and package tests)
* [http://mrxlabs.com/ MRX Labs] (Pricing not public)
* [http://oganalytical.com/ Oregon Growers Analytical] (Pricing not public)
* [http://phlabsoregon.com/ pH Labs] (($60–$375, single and package tests)
* [http://www.rosecitylabs.com Rose City Laboratories] ($25–$360, single and package tests)
 
===Support services===
The following entities are known to provide support services to cannabis testing labs:
 
* [http://www.vitalescientific.com/services/cannabis/ Vitale Scientific Associates] (IL, NM, PA, TN, VA)
 
 
==Future of cannabis testing and market trends==
 
 
==Resources==
 
===Key reading===
 
* {{cite web |url=https://www.aphl.org/aboutAPHL/publications/Documents/EH-Guide-State-Med-Cannabis-052016.pdf |format=PDF |title=Guidance for State Medical Cannabis Testing Programs |author=Association of Public Health Laboratories |pages=35 |date=May 2016}}
 
* {{cite journal |title=''Cannabis sativa'': The plant of the thousand and one molecules |journal=Frontiers in Plant Medicine |author=Andre, C.M.; Hausman, J.-F.; Guerriero, G. |volume=7 |pages=19 |year=2016 |doi=10.3389/fpls.2016.00019 |pmid=26870049 |pmc=PMC4740396}}
 
* {{cite web |url=https://www.aocs.org/stay-informed/read-inform/featured-articles/the-highs-and-lows-of-cannabis-testing-october-2016 |title=The Highs and Lows of Cannabis Testing |author=Cassiday, L. |work=INFORM |publisher=American Oil Chemists' Society |date=October 2016}}
 
 
===Reference material===
 
* {{cite web |url=http://www.ncsl.org/bookstore/state-legislatures-magazine/marijuana-deep-dive.aspx |title=Marijuana Deep Dive |author=National Conference of State Legislatures |date=2016}}
 
* {{cite web |url=https://www.whitehouse.gov/ondcp/marijuanainfo |archiveurl=http://web.archive.org/web/20170117133745/https://www.whitehouse.gov/ondcp/marijuanainfo |title=Marijuana Resource Center |author=Office of National Drug Control Policy |archivedate=17 January 2017}}
 
* {{cite book |url=http://www.who.int/substance_abuse/publications/cannabis/en/ |title=The health and social effects of nonmedical cannabis use |author=World Health Organization |editor=Hall, W.; Renström, M.; Poznyak, V |publisher=World Health Organization |pages=95 |year=2016 |isbn=978921510240}}
 
====Law and regulation====
 
* {{cite web |url=http://medicalmarijuana.procon.org/view.resource.php?resourceID=000881 |title=28 Legal Medical Marijuana States and DC: Laws, Fees, and Possession Limits |work=ProCon.org |date=28 December 2016}}
 
* {{cite web |url=https://www.leafly.com/news/cannabis-101/home-cannabis-cultivation-laws-a-state-by-state-guide |title=Home Cannabis Cultivation Laws: A State-by-State Guide |work=Leafly - Cannabis 101 |publisher=Leafly Holdings, Inc}}
 
* {{cite web |url=http://smallbusiness.findlaw.com/business-laws-and-regulations/marijuana-business-licenses-permits-and-planning.html |title=Marijuana Business: Licenses, Permits, and Planning |work=FindLaw |publisher=Thomson Reuters}}
 
* {{cite web |url=http://www.ncsl.org/research/health/state-medical-marijuana-laws.aspx |title=State Medical Marijuana Laws |author=National Conference of State Legislatures |date=09 November 2016}}
 
* {{cite web |url=https://www.leafly.com/news/industry/leaflys-state-by-state-guide-to-cannabis-testing-regulations |title=Leafly’s State-by-State Guide to Medical Cannabis Testing Regulations |author=Rough, Lisa |work=Leafly - Industry |publisher=Leafly Holdings, Inc}}
 
 
====Publications and blogs====
 
* ''[https://www.cannabisindustryjournal.com/ Cannabis Industry Journal]''
* ''[http://mjbizdaily.com/ Marijuana Business Daily]''
* ''[http://www.thompsoncoburn.com/insights/blogs/tracking-cannabis Tracking Cannabis]'' - Thomas Coburn LLP
 
====Standards and guidance====
 
* {{cite web |url=http://www.mass.gov/eohhs/docs/dph/quality/medical-marijuana/lab-protocols/finished-mmj/final-revised-mdph-mmj-mips-protocol.pdf |format=PDF |title=Protocol for Sampling and Analysis of Finished Medical Marijuana Products and Marijuana-Infused Products for Massachusetts Registered Medical Marijuana Dispensaries |author=Bureau of Health Care Safety and Quality |publisher=Massachusetts Department of Public Health |pages=25 |date=05 February 2016}}
 
* {{cite web |url=http://www.ahpa.org/Portals/0/pdfs/AHPA_Recommendations_for_Regulators_Cannabis_Operations.pdf |format=PDF |title=Recommendations for Regulators – Cannabis Operations |author=Cannabis Committee, AHPA |publisher=American Herbal Products Association |date=02 February 2016}}
 
* {{cite book |url=http://www.herbal-ahp.org/order_online.htm |title=''Cannabis Inflorescence'': ''Cannabis'' spp. |publisher=American Herbal Pharmacopoeia |editor=Upton, R.; Craker, L.; ElSohly, M. et al. |year=2014 |isbn=1929425333}}
 
* {{cite web |url=http://www.fda.gov/Food/FoodScienceResearch/LaboratoryMethods/ucm2006955.htm |title=Pesticide Analytical Manual (PAM) |publisher=United States Food and Drug Administration |date=07 June 2015 |accessdate=14 February 2017}}
 
* {{cite web |url=http://www.fda.gov/downloads/Food/FoodborneIllnessContaminants/Metals/UCM272693.pdf |format=PDF |title=Analysis of Foods for As, Cd, Cr, Hg and Pb by Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) |publisher=United States Food and Drug Administration, Center for Food Safety and Applied Nutrition |date=25 April 2011}}
 
* {{cite web |url=https://archive.epa.gov/pesticides/methods/rammethods/web/html/ram12b.html |title=Residue Analytical Methods (RAM) |publisher=United States Environmental Protection Agency |date=20 February 2016 |accessdate=14 February 2017}}
 
 
====Accreditation and certification====
 
* {{cite web |url=http://patientfocusedcertification.org/ |title=Patient Focused Certification |author=Americans for Safe Access Foundation}}
 
* {{cite web |url=http://www.theemeraldtest.com/ |title=The Emerald Test |author=Emerald Scientific, LLC}}
 
* {{cite web |url=http://www.pjlabs.com/accreditation-programs/medical-marijuana-testing |title=Accreditation Programs: Medical Marijuana Testing |author=Perry Johnson Laboratory Accreditation, Inc}}
 
 
====Testing====
 
* {{cite web |url=https://www.a2la.org/appsweb/cannabis_testing.cfm |title=Cannabis Testing Laboratory Accreditation |author=American Association of Laboratory Accreditation}}
 
* {{cite web |url=https://www.aphl.org/aboutAPHL/publications/Documents/EH-Guide-State-Med-Cannabis-052016.pdf |format=PDF |title=Guidance for State Medical Cannabis Testing Programs |author=Association of Public Health Laboratories |pages=35 |date=May 2016}}
 
* {{cite web |url=http://lcb.wa.gov/publications/Marijuana/BOTEC%20reports/1a-Testing-for-Contaminants-Final-Revised.pdf |format=PDF |title=Testing ''Cannabis'' for Contaminants |author=Daley, P.; Lampach, D.; Sguerra, S. |publisher=BOTEC Analysis Corp |date=12 September 2013}}
 
* {{cite web |url=https://www.wadsworth.org/sites/default/files/WebDoc/576578963/MML-300-01.pdf |format=PDF |title=Measurement of Phytocannabinoids using HPLC-PDA, NYS DOH MML-300 |author=Division of Environmental Health Sciences, Laboratory of Organic Analytical Chemistry |publisher=New York State Department of Health |pages=34 |date=03 November 2015}}
 
* {{cite web |url=https://www.wadsworth.org/sites/default/files/WebDoc/1495494332/MML-301-01.pdf |format=PDF |title=Medical marijuana sample preparation protocols for potency analysis, NYS DOH MML-301 |author=Division of Environmental Health Sciences, Laboratory of Organic Analytical Chemistry |publisher=New York State Department of Health |pages=13 |date=03 November 2015}}
 
* {{cite web |url=https://www.wadsworth.org/sites/default/files/WebDoc/359205661/MML-303-01.pdf |format=PDF |title=Measurement of Mycotoxins by LC-MS/MS, NYS DOH MML-303  |author=Division of Environmental Health Sciences, Laboratory of Organic Analytical Chemistry |publisher=New York State Department of Health |pages=43 |date=18 November 2015}}
 
* {{cite web |url=http://www.unodc.org/documents/scientific/ST-NAR-40-Ebook.pdf |format=PDF |title=Recommended methods for the identification and analysis of cannabis and cannabis products |author=United Nations Office on Drugs and Crime |date=September 2009}} (Note year; still provides historical perspective)
 
* {{cite web |url=https://www.unodc.org/documents/scientific/STNAR48_Synthetic_Cannabinoids_ENG.pdf |format=PDF |title=Recommended methods for the identification and analysis of synthetic cannabinoid receptor agonists in seized materials |author=United Nations Office on Drugs and Crime |date=June 2013}}
 
 
====Laboratories====
 
* {{cite web |url=http://mjbizdaily.com/industry-directory/testing-lab-services/ |title=Marijuana Testing & Lab Services |work=Marijuana Business Daily |publisher=Anne Holland Ventures, Inc}}
 
 
===Conferences and trade shows===
 
* {{cite web |url=http://www.cannabisbusinesssummit.com/oakland-2017/ |title=Cannabis Business Summit and Expo |publisher=National Cannabis Industry Association}}
 
* {{cite web |url=https://www.cannabisscienceconference.com/ |title=Cannabis Science Conference |publisher=jCanna, Inc}}
 
* {{cite web |url=http://www.theemeraldconference.com/ |title=Emerald Conference |publisher=Emerald Scientific, LLC}}
 
* {{cite web |url=https://www.csupueblo.edu/institute-of-cannabis-research/2017-conference/ |title=Institute of Cannabis Research Conference |publisher=Colorado State University - Pueblo}}
 
* {{cite web |url=http://internationalcbc.com/ |title=International Cannabis Business Conference |publisher=International Conferences Group, LLC}}
 
* {{cite web |url=https://mjbizconference.com/ |title=Marijuana Business Conference & Expo |publisher=Anne Holland Ventures, Inc}}
 
* {{cite web |url=http://patientsoutoftime.org/2017-national-clinical-conference-on-cannabis-therapeutics/ |title=National Clinical Conference on Cannabis Therapeutics |publisher=Patients Out of Time}}
 
* {{cite web |url=https://www.compassionatecertificationcenters.com/conference-overview/ |title=World Medical Marijuana Business Conference & Expo |publisher=Present Syndikos LLC}}
 
===Associations, organizations, and interest groups===
 
* [http://www.agriculturalgenomics.org/ Agricultural Genomics Foundation]
* [http://americancannabisnursesassociation.org/ American Cannabis Nurses Association]
* [https://www.aocs.org/ American Oil Chemists Society]
* [http://www.safeaccessnow.org/ Americans for Safe Access]
* [http://www.cacannabislabs.com/ Association of Commercial Cannabis Laboratories]
* [http://www.australiancannabisindustry.org/ Australian Cannabis Industry Association]
* [http://www.chascience.com/ Cannabis Horticultural Association]
* [https://www.drugabuse.gov/about-nida/organization/workgroups-interest-groups-consortia/cannabis-science-interest-csi-group Cannabis Science Interest (CSI) Group], part of the National Institute on Drug Abuse
* [http://cannabisandsocialpolicy.org/ Center for the Study of Cannabis and Social Policy]
* [http://www.cocannabischamber.com/ Colorado Cannabis Chamber of Commerce]
* [https://www.cannabis-med.org/ International Association for Cannabinoid Medicines]
* [http://icrs.co/ International Cannabinoid Research Society]
* [http://thecannabisindustry.org/ National Cannabis Industry Association]
* [http://norml.org/ NORML]
* [http://patientsoutoftime.org/ Patients Out of Time]
* [https://www.projectcbd.org/ Project CBD]
* [https://learnaboutsam.org/ Smart Approaches to Marijuana]
* [http://cannabisclinicians.org/ Society of Cannabis Clinicians]
* [https://themedicalcannabisinstitute.org/ The Medical Cannabis Institute]
* [http://www.vetscp.org/ Veterans Cannabis Project]
 
===Producers and vendors===
 
 
 
===Hardware vendors===
 
* [http://www.emeraldscientific.com/ Emerald Scientific, LLC]
* [http://gentechscientific.com/cannabis-testing/index.php GenTech Scientific]
* [http://www.restek.com/Landing-Pages/Medical-Marijuana Restek Corporation]
* [http://www.ssi.shimadzu.com/industry/industry_cannabistesting.cfm Shimadzu Scientific Instruments, Inc.]
 
===Software vendors===
 
====LIMS====
 
====Seed-to-sale====
 
 
===LIMSpec===


:'''Clinical diagnostics''': "From an industry and regulatory perspective, however, only the intended uses supported from the media manufacturer can be supported from AI applications, unless otherwise justified and substantive evidence is presented for additional claims support. This means strict adherence to specimen type and incubation conditions. Considering that the media was initially developed for human assessment using the well-trained microbiologist eye, and not an advanced imaging system with or without AI, this paradigm should shift to allow advancements in technology to challenge the status-quo of decreasing media read-times especially, as decreased read-times assist with laboratory turnaround times and thus patient management. Perhaps with an increasing body of evidence to support any proposed indications for use, either regulatory positions should be challenged, or manufacturers of media and industry AI-development specialists should work together to advance the field with new indications for use.
:While the use of AI in the laboratory setting can be highly beneficial there are still some issues to be addressed. The first being phenotypically distinct single organism polymorphisms that may be interpreted by AI as separate organisms, as may also be the case for a human assessment, as well as small colony variant categorization. As detailed earlier, the broader the inputs, the greater the generalization of the model, and the higher the likelihood of algorithm accuracy. In that respect, understanding and planning around these design constraints is critical for ultimate deployment of algorithms. Additionally, expecting an AI system to correctly categorize “contamination” is a difficult task as often this again seemingly innocuous decision is dependent on years of experience and understanding the specimen type and the full clinical picture with detailed clinical histories. In this respect, a fully integrated AI-LIS system where all data is available may assist, but it is currently not possible to gather this granular detail needed to make this assessment reliable."<ref name=":7" />
:'''Clinical diagnostics and pathology''': "Well, if I’ve learned anything in my research into this topic, it’s that AI implementation needs to be a two-way street. First, any company who is active in this space must reach out to pathologists and laboratory medicine professionals to understand their daily workflows, needs, and pain points in as much detail as possible. Second, pathologists, laboratory medicine professionals, and educators must all play their important part – willingly offering their time and expertise when it is sought or proactively getting involved. And finally, it’s clear that there is an imbalanced focus on certain issues – with privacy, respect, and sustainability falling by the wayside."<ref name=":10">{{Cite web |last=Lee, G.F. |date=10 October 2022 |title=The Robot May See You Now: It’s time to stop and think about the ethics of artificial intelligence |work=The Pathologist |url=https://thepathologist.com/outside-the-lab/the-robot-may-see-you-now |accessdate=17 February 2023}}</ref>
:'''Healthcare''': "While we are encouraged by the promise shown by AI in healthcare, and more broadly welcome the use of digital technologies in improving clinical outcomes and health system productivity, we also recognize that caution must be exercised when introducing any new healthcare technology. Working with colleagues across the NHS Transformation Directorate, as well as the wider AI community, we have been developing a framework to evaluate AI-enabled solutions in the health and care policy context. The aim of the framework is several-fold but is, at its core, a tool with which to highlight to healthcare commissioners, end users, patients and members of the public the considerations to be mindful when introducing AI to healthcare settings."<ref>{{Cite journal |last=Chada |first=Bharadwaj V |last2=Summers |first2=Leanne |date=2022-10-10 |title=AI in the NHS: a framework for adoption |url=https://www.rcpjournals.org/lookup/doi/10.7861/fhj.2022-0068 |journal=Future Healthcare Journal |language=en |pages=fhj.2022–0068 |doi=10.7861/fhj.2022-0068 |issn=2514-6645 |pmc=PMC9761451 |pmid=36561823}}</ref>
:'''Most any lab''': A code of AI ethics should address objectivity, privacy, transparency, accountability, and sustainability in any AI implementation.<ref name=":10" />
:'''Most any lab''': "Another approach is to implement an AI program alongside a manual process, assessing its performance along the way, as a means to ease into using the program. 'I think one of the most impactful things that laboratorians can do today is to help make sure that the lab data that they’re generating is as robust as possible, because these AI tools rely on new training sets, and their performance is really only going to be as good as the training data sets they’re given,' Stoffel said."<ref name=":8" />


==References==
==References==
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{{Reflist|colwidth=30em}}
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Latest revision as of 19:33, 17 February 2023

Sandbox begins below

  • Discussion and practical use of artificial intelligence (AI) in the laboratory is, perhaps to the surprise of some, not a recent phenomena. In the mid-1980s, researchers were developing computerized AI systems able "to develop automatic decision rules for follow-up analysis of [clinical laboratory] tests depending on prior information, thus avoiding the delays of traditional sequential testing and the costs of unnecessary parallel testing."[1] In fact, discussion of AI in general was ongoing even in the mid-1950s.[2][3]
  • Hiring demand for laboratorians with AI experience (2015–18) has historically been higher in non-healthcare industries, such as manufacturing, mining, and agriculture, shedding a light on how AI adoption in the clinical setting may be lacking. According to the Brookings Institute, "Even for the relatively-skilled job postings in hospitals, which includes doctors, nurses, medical technicians, research lab workers, and managers, only approximately 1 in 1,250 job postings required AI skills." They add: "AI adoption may be slow because it is not yet useful, or because it may not end up being as useful as we hope. While our view is that AI has great potential in health care, it is still an open question."[4]
  • Today, AI is being practically used in not only clinical diagnostic laboratories but also clinical research labs, life science labs, and research and development (R&D) labs, and more. Practical uses of AI can be found in:
clinical research labs[5]
hospitals[5][6]
medical diagnostics labs[6][7][8][9][10][11]
chromatography labs[11]
biology and life science labs[12]
medical imaging centers[13]
ophthalmology clinics[14]
reproduction clinics[15][16][17]
digital pathology labs[18]
material testing labs[19][20][21]
chemical experimentation and molecular discovery labs[21][22][23]
quantum physics labs[24]
  • What's going on in these labs?
Materials science: The creation of "a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions ..."[19]
Materials science: "Most of the applications of [machine learning (ML)] in chemical and materials sciences, as we have said, feature supervised learning algorithms. The goal there is to supplement or replace traditional modeling methods, at the quantum chemical or classical level, in order to predict the properties of molecules or materials directly from their structure or their chemical composition ... Our research group was applying the same idea on a narrower range of materials, trying to confirm that for a given chemical composition, geometrical descriptors of a material’s structure could lead to accurate predictions of its mechanical features."[20]
Life science: "In biological experiments, we generally cannot as easily declare victory, but we can use the systems biology approach of cycling between experimentation and modelling to see which sequences, when tested, are most likely to improve the model. In artificial intelligence, this is called active learning, and it has some similarity to the way in which we as humans learn as infants: we get some help from parents and teachers, but mainly model the world around us by exploring it and interacting with it. Ideally then, we would recreate such an environment for our machine learning algorithms in the laboratory, where we start with an initial ‘infant’ model of a certain regulatory system or protein function and let the computer decide what sequence designs to try out – a deep learning version of the ‘robot scientist’. Microbes are ideal organisms for such an approach, given the ease and speed with which they can be grown and genetically manipulated. Combined with laboratory automation, many microbial experiments can (soon) be performed with minimal human intervention, ranging from strain construction and screening, such as operated by Amyris, Gingko, Transcriptic, etc., to full-genome engineering or even the design of microbial ecologies."[12]
Digital pathology: "The collaboration combines two AI solutions, VistaPath’s Sentinel, the world’s first automated tissue grossing platform, and Gestalt’s AI Requisition Engine (AIRE), a leading-edge AI algorithm for accessioning, to raise the bar in AI-driven pathology digitization. Designed to make tissue grossing faster and more accurate, VistaPath’s Sentinel uses a high-quality video system to assess specimens and create a gross report 93% faster than human technicians with 43% more accuracy. It not only improves on quality by continuously monitoring the cassette, container, and tissue to reduce mislabeling and specimen mix-up, but also increases traceability by retaining original images for downstream review."[25]
Chemistry and molecular science: "The benefits of combining automated experimentation with a layer of artificial intelligence (AI) have been demonstrated for flow reactors, photovoltaic films, organic synthesis, perovskites and in formulation problems. However, so far no approaches have integrated mobile robotics with AI for chemical experiments. Here, we built Bayesian optimization into a mobile robotic workflow to conduct photocatalysis experiments within a ten-dimensional space."[22]
Chemistry and immunology: "Chemistry and immunology laboratories are particularly well-suited to leverage machine learning because they generate large, highly structured data sets, Schulz and others wrote in a separate review paper. Labor-intensive processes used for interpretation and quality control of electrophoresis traces and mass spectra could benefit from automation as the technology improves, they said. Clinical chemistry laboratories also generate digital images—such as urine sediment analysis—that may be highly conducive to semiautomated analyses, given advances in computer vision, the paper noted."[26]
Clinical research: "... retrospective analysis of existing patient data for descriptive and clustering purposes [and] automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses ..."[5]
Clinical research: "AI ... offers a further layer to the laboratory system by analyzing all experimental data collected by experiment devices, whether it be a sensor or a collaborative robot. From data collected, AI is able to produce hypotheses and predict which combination of materials or temperature is desired for the experiment. In short, this system will allow scientists to be aided by a highly intelligent system which is constantly monitoring and analyzing the experimental output. In this way, AI will help an experiment from its inception to conclusion."[27]
Clinical research/medical diagnostics: "Artificial intelligence (AI) in the laboratory is primarily used to make sense of big data, the almost impossibly large sets of data that biologists and pharmaceutical R&D teams are accustomed to working with. AI algorithms can parse large amounts of data in a short amount of time and turn that data into visualizations that viewers can easily understand. In certain data-intensive fields, such as genomic testing and virus research, AI algorithms are the best way to sort through the data and do some of the pattern recognition work."[28]
Medical diagnostics: Development and implementation of clinical decision support systems [5][6]
Medical diagnostics: "Finally, in the laboratory, AI reduces the number of unnecessary blood samples when diagnosing infection. Instead of the 'gold standard blood sample' that takes 24-72 hours, the algorithm can predict the outcome of the blood sample with almost 80% accuracy based on demographics, vital signs, medications, and laboratory and radiology results. These are all examples of how Artificial Intelligence can be used to test better and faster with information that already exists. This saves time and costs."[10]
Medical diagnostics: "Chang sees two overarching classes of AI models: those that tackle internal challenges in the lab, such as how to deliver more accurate results to clinicians; and those that seek to identify cohorts of patients and care processes to close quality gaps in health delivery systems. The lab, however, 'isn’t truly an island,' said Michelle Stoffel, MD, PhD, associate chief medical information officer for laboratory medicine and pathology at M Health Fairview and the University of Minnesota in Minneapolis. 'When other healthcare professionals are working with electronic health records or other applications, there could be AI-driven tools, or algorithms used by an institution’s systems that may draw on laboratory data.'"[26]
Medical diagnostics: AI is used for the formulation of reference ranges, improvement of quality control, and automated interpretation of results. "Continuous monitoring of specimen acceptability, collection and transport can result in the prompt identification and correction of problems, leading to improved patient care and a reduction in unnecessary redraws and delays in reporting results."[8]
Reproduction science: "The field of AI is the marriage of humans and computers while reproductive medicine combines clinical medicine and the scientific laboratory of embryology. The application of AI has the potential to disconnect healthcare professionals from patients through algorithms, automated communication, and clinical imaging. However, in the embryology laboratory, AI, with its focus on gametes and embryos, can avoid the same risk of distancing from the patient. Areas of application of AI in the laboratory would be to enhance and automate embryo ranking through analysis of images, the ultimate goal being to predict successful implantation. Might such a trend obviate the need for embryo morphological assessment, time-lapse imaging and preimplantation genetic testing for aneuploidy (PGT-A), including mosaicism. Additionally, AI could assist with automation through analysis of testicular sperm samples searching for viable gametes, embryo grading uniformity."[15]
Chromatography-heavy sciences: " A great example of this is AI in the Liquid Chromatography Mass Spectrometry (LC-MS) field. LC-MS is a great tool used to measure various compounds in the human body, including everything from hormone levels to trace metals. One of the ways AI has already integrated with LC-MS is how it cuts down on the rate limiting steps of LC-MS, which more often than not are sample prep and LC separations. One system that Physicians Lab has made use of is parallel processing using SCIEX MPX 2.0 High Throughput System. This system can couple parallel runs with one LCMS instrument, resulting in twice the speed with no loss to accuracy. It can do this by staggering two runs either using the same method, or different methods entirely. What really makes this system great is its ability to automatically detect carryover and inject solvent blanks to clean the instrument. The system will then continue its analyzing, while automatically reinjecting samples that may be affected by the carryover. It will also flag high concentration without user input, allowing for easy detection of possibly faulty samples. This allows it to operate without users from startup to shut down. Some of the other ways that it can be used to increase efficiency are by using integrated network features to work on anything from streamlining management to increased throughput."[11]
Most any lab: "Predictive analytics, for example, is one tool that the Pistoia Alliance is using to better understand laboratory instruments and how they might fail over time... With the right data management strategies and careful consideration of metadata, how to best store data so that it can be used in future AI and ML workflows is essential to the pursuit of AI in the laboratory. Utilizing technologies such as LIMS and ELN enables lab users to catalogue data, providing context and instrument parameters that can then be fed into AI or ML systems. Without the correct data or with mismatched data types, AI and ML will not be possible, or at the very least, could provide undue bias trying to compare data from disparate sources."[29]
Most any lab: "When the actionable items are automatically created by Optima, the 'engine' starts working. An extremely sophisticated algorithm is able to assign the tasks to the resources, both laboratory personnel and instruments, according to the system configuration. Optima, thanks to a large amount of time dedicated to research the best way to automate this critical process, is able to automate most of the lab resource scheduling."[30]
  • A number of challenges exist in the realm of effectively and securely implementing AI in the laboratory. This includes:
Ethical and privacy challenges[5][26][31]
Algorithmic limitations[4]
Data access limitations, including "where to get it, how to share it, and how to know when you have enough to train a machine-learning system that will produce good results"[4][26][32][33]
Data integration and transformation issues[5][33]
Regulatory barriers[4][7]
Misaligned incentives[4]
Lack of knowledgeable/skilled talent[5][26][32][33]
Cost of skilled talent and infrastructure for maintaining and updating AI systems[26]
Legacy systems running outdated technologies[32]
Lack of IT systems or specialized software systems[33]
Lack of standardized, best practices-based methods of validating algorithms[26]
Failure to demonstrate real-world performance[7]
Failure to meet the needs of the professionals using it[7]
  • Given those challenges, some considerations should be made about implementing AI-based components in the laboratory. Examples include:
Clinical diagnostics: "From an industry and regulatory perspective, however, only the intended uses supported from the media manufacturer can be supported from AI applications, unless otherwise justified and substantive evidence is presented for additional claims support. This means strict adherence to specimen type and incubation conditions. Considering that the media was initially developed for human assessment using the well-trained microbiologist eye, and not an advanced imaging system with or without AI, this paradigm should shift to allow advancements in technology to challenge the status-quo of decreasing media read-times especially, as decreased read-times assist with laboratory turnaround times and thus patient management. Perhaps with an increasing body of evidence to support any proposed indications for use, either regulatory positions should be challenged, or manufacturers of media and industry AI-development specialists should work together to advance the field with new indications for use.
While the use of AI in the laboratory setting can be highly beneficial there are still some issues to be addressed. The first being phenotypically distinct single organism polymorphisms that may be interpreted by AI as separate organisms, as may also be the case for a human assessment, as well as small colony variant categorization. As detailed earlier, the broader the inputs, the greater the generalization of the model, and the higher the likelihood of algorithm accuracy. In that respect, understanding and planning around these design constraints is critical for ultimate deployment of algorithms. Additionally, expecting an AI system to correctly categorize “contamination” is a difficult task as often this again seemingly innocuous decision is dependent on years of experience and understanding the specimen type and the full clinical picture with detailed clinical histories. In this respect, a fully integrated AI-LIS system where all data is available may assist, but it is currently not possible to gather this granular detail needed to make this assessment reliable."[9]
Clinical diagnostics and pathology: "Well, if I’ve learned anything in my research into this topic, it’s that AI implementation needs to be a two-way street. First, any company who is active in this space must reach out to pathologists and laboratory medicine professionals to understand their daily workflows, needs, and pain points in as much detail as possible. Second, pathologists, laboratory medicine professionals, and educators must all play their important part – willingly offering their time and expertise when it is sought or proactively getting involved. And finally, it’s clear that there is an imbalanced focus on certain issues – with privacy, respect, and sustainability falling by the wayside."[31]
Healthcare: "While we are encouraged by the promise shown by AI in healthcare, and more broadly welcome the use of digital technologies in improving clinical outcomes and health system productivity, we also recognize that caution must be exercised when introducing any new healthcare technology. Working with colleagues across the NHS Transformation Directorate, as well as the wider AI community, we have been developing a framework to evaluate AI-enabled solutions in the health and care policy context. The aim of the framework is several-fold but is, at its core, a tool with which to highlight to healthcare commissioners, end users, patients and members of the public the considerations to be mindful when introducing AI to healthcare settings."[34]
Most any lab: A code of AI ethics should address objectivity, privacy, transparency, accountability, and sustainability in any AI implementation.[31]
Most any lab: "Another approach is to implement an AI program alongside a manual process, assessing its performance along the way, as a means to ease into using the program. 'I think one of the most impactful things that laboratorians can do today is to help make sure that the lab data that they’re generating is as robust as possible, because these AI tools rely on new training sets, and their performance is really only going to be as good as the training data sets they’re given,' Stoffel said."[26]

References

  1. Berger-Hershkowitz, H.; Neuhauser, D. (1987). "Artificial intelligence in the clinical laboratory". Cleveland Clinic Journal of Medicine 54 (3): 165–166. doi:10.3949/ccjm.54.3.165. ISSN 0891-1150. PMID 3301059. https://www.ccjm.org/content/54/3/165. 
  2. Minsky, M. (17 December 1956). Heuristic Aspects of the Artificial Intelligence Problem. Ed Services Technical Information Agency. https://books.google.com/books?hl=en&lr=&id=fvWNo6_IZGUC&oi=fnd&pg=PA1. Retrieved 16 February 2023. 
  3. Minsky, Marvin (1 January 1961). "Steps toward Artificial Intelligence". Proceedings of the IRE 49 (1): 8–30. doi:10.1109/JRPROC.1961.287775. ISSN 0096-8390. http://ieeexplore.ieee.org/document/4066245/. 
  4. 4.0 4.1 4.2 4.3 4.4 Goldfarb, A.; Teodoridis, F. (9 March 2022). "Why is AI adoption in health care lagging?". Series: The Economics and Regulation of Artificial Intelligence and Emerging Technologies. Brookings Institute. https://www.brookings.edu/research/why-is-ai-adoption-in-health-care-lagging/. Retrieved 17 February 2023. 
  5. 5.0 5.1 5.2 5.3 5.4 5.5 5.6 Damiani, A.; Masciocchi, C.; Lenkowicz, J.; Capocchiano, N. D.; Boldrini, L.; Tagliaferri, L.; Cesario, A.; Sergi, P. et al. (7 December 2021). "Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator". Frontiers in Computer Science 3: 768266. doi:10.3389/fcomp.2021.768266. ISSN 2624-9898. https://www.frontiersin.org/articles/10.3389/fcomp.2021.768266/full. 
  6. 6.0 6.1 6.2 University of California, San Francisco; Adler-Milstein, Julia; Aggarwal, Nakul; University of Wisconsin-Madison; Ahmed, Mahnoor; National Academy of Medicine; Castner, Jessica; Castner Incorporated et al. (29 September 2022). "Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis". NAM Perspectives 22 (9). doi:10.31478/202209c. PMC PMC9875857. PMID 36713769. https://nam.edu/meeting-the-moment-addressing-barriers-and-facilitating-clinical-adoption-of-artificial-intelligence-in-medical-diagnosis. 
  7. 7.0 7.1 7.2 7.3 Government Accountability Office (GAO); National Academy of Medicine (NAM) (September 2022). "Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics" (PDF). Government Accountability Office. https://www.gao.gov/assets/gao-22-104629.pdf. Retrieved 16 February 2023. 
  8. 8.0 8.1 Wen, Xiaoxia; Leng, Ping; Wang, Jiasi; Yang, Guishu; Zu, Ruiling; Jia, Xiaojiong; Zhang, Kaijiong; Mengesha, Birga Anteneh et al. (24 September 2022). "Clinlabomics: leveraging clinical laboratory data by data mining strategies" (in en). BMC Bioinformatics 23 (1): 387. doi:10.1186/s12859-022-04926-1. ISSN 1471-2105. PMC PMC9509545. PMID 36153474. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04926-1. 
  9. 9.0 9.1 DeYoung, B.; Morales, M.; Giglio, S. (4 August 2022). "Microbiology 2.0–A “behind the scenes” consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories". Frontiers in Microbiology 13: 976068. doi:10.3389/fmicb.2022.976068. ISSN 1664-302X. PMC PMC9386241. PMID 35992715. https://www.frontiersin.org/articles/10.3389/fmicb.2022.976068/full. 
  10. 10.0 10.1 Schut, M. (1 December 2022). "Get better with bytes". Amsterdam UMC. https://www.amsterdamumc.org/en/research/news/get-better-with-bytes.htm. Retrieved 16 February 2023. 
  11. 11.0 11.1 11.2 Albano, V.; Morris, C.; Kent, T. (6 December 2019). "Calculations to Diagnosis: The Artificial Intelligence Shift That’s Already Happening". Physicians Lab. https://physicianslab.com/calculations-to-diagnosis-the-artificial-intelligence-shift-thats-already-happening/. Retrieved 16 February 2023. 
  12. 12.0 12.1 de Ridder, Dick (1 January 2019). "Artificial intelligence in the lab: ask not what your computer can do for you" (in en). Microbial Biotechnology 12 (1): 38–40. doi:10.1111/1751-7915.13317. PMC PMC6302702. PMID 30246499. https://onlinelibrary.wiley.com/doi/10.1111/1751-7915.13317. 
  13. Brandao-de-Resende, C.; Bui, M.; Daneshjou, R. et al. (11 October 2022). "AI Webinar: Clinical Adoption of AI Across Image Producing Specialties". Society for Imaging Informatics in Medicine. https://siim.org/page/22w_clinical_adoption_of_ai. 
  14. He, Mingguang; Li, Zhixi; Liu, Chi; Shi, Danli; Tan, Zachary (1 July 2020). "Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge" (in en). Asia-Pacific Journal of Ophthalmology 9 (4): 299–307. doi:10.1097/APO.0000000000000301. ISSN 2162-0989. https://journals.lww.com/10.1097/APO.0000000000000301. 
  15. 15.0 15.1 Trolice, Mark P.; Curchoe, Carol; Quaas, Alexander M (1 July 2021). "Artificial intelligence—the future is now" (in en). Journal of Assisted Reproduction and Genetics 38 (7): 1607–1612. doi:10.1007/s10815-021-02272-4. ISSN 1058-0468. PMC PMC8260235. PMID 34231110. https://link.springer.com/10.1007/s10815-021-02272-4. 
  16. European Society of Human Reproduction and Embryology (6 July 2022). "Annual Meeting 2022: Artificial intelligence in embryology and ART". Focus on Reproduction. https://www.focusonreproduction.eu/article/ESHRE-News-22AI. Retrieved 16 February 2023. 
  17. Hinckley, M. (17 March 2021). "Applying AI (Artificial Intelligence) in the Lab for Better IVF Success". Reproductive Science Center Blog. Reproductive Science Center of the Bay Area. https://rscbayarea.com/blog/applying-ai-for-better-ivf-success. Retrieved 16 February 2023. 
  18. Yousif, M.; McClintock, D.S.; Yao, K. (2021). "Artificial intelligence is the key driver for digital pathology adoption". Clinical Laboratory Int. PanGlobal Media. https://clinlabint.com/artificial-intelligence-is-the-key-driver-for-digital-pathology-adoption/. Retrieved 16 February 2023. 
  19. 19.0 19.1 MacLeod, B. P.; Parlane, F. G. L.; Morrissey, T. D.; Häse, F.; Roch, L. M.; Dettelbach, K. E.; Moreira, R.; Yunker, L. P. E. et al. (15 May 2020). "Self-driving laboratory for accelerated discovery of thin-film materials" (in en). Science Advances 6 (20): eaaz8867. doi:10.1126/sciadv.aaz8867. ISSN 2375-2548. PMC PMC7220369. PMID 32426501. https://www.science.org/doi/10.1126/sciadv.aaz8867. 
  20. 20.0 20.1 Chibani, Siwar; Coudert, François-Xavier (1 August 2020). "Machine learning approaches for the prediction of materials properties" (in en). APL Materials 8 (8): 080701. doi:10.1063/5.0018384. ISSN 2166-532X. http://aip.scitation.org/doi/10.1063/5.0018384. 
  21. 21.0 21.1 Mullin, R. (28 March 2021). "The lab of the future is now". Chemical & Engineering News 99 (11). Archived from the original on 06 May 2022. https://web.archive.org/web/20220506192926/http://cen.acs.org/business/informatics/lab-future-ai-automated-synthesis/99/i11. Retrieved 16 February 2023. 
  22. 22.0 22.1 Burger, Benjamin; Maffettone, Phillip M.; Gusev, Vladimir V.; Aitchison, Catherine M.; Bai, Yang; Wang, Xiaoyan; Li, Xiaobo; Alston, Ben M. et al. (9 July 2020). "A mobile robotic chemist" (in en). Nature 583 (7815): 237–241. doi:10.1038/s41586-020-2442-2. ISSN 0028-0836. https://www.nature.com/articles/s41586-020-2442-2.epdf?sharing_token=HOkIS6P5VIAo2_l3nRELmdRgN0jAjWel9jnR3ZoTv0Nw4yZPDO1jBpP52iNWHbb8TakOkK906_UHcWPTvNxCmzSMpAYlNAZfh29cFr7WwODI2U6eWv38Yq2K8odHCi-qwHcEDP18OjAmH-0KgsVgL5CpoEaQTCvbmhXDSyoGs6tIMe1nuABTeP58z6Ck3uULcdCtVQ66X244FsI7uH8GnA%3D%3D&tracking_referrer=cen.acs.org. 
  23. Lemonick, S. (6 April 2020). "Exploring chemical space: Can AI take us where no human has gone before?". Chemical & Engineering News 98 (13). Archived from the original on 29 July 2020. https://web.archive.org/web/20200729004137/https://cen.acs.org/physical-chemistry/computational-chemistry/Exploring-chemical-space-AI-take/98/i13. Retrieved 16 February 2023. 
  24. Doctrow, B. (16 December 2019). "Artificial intelligence in the laboratory". PNAS Science Sessions. https://www.pnas.org/post/podcast/artificial-intelligence-laboratory. Retrieved 16 February 2023. 
  25. VistaPath (28 July 2022). "VistaPath Launches New Collaboration with Gestalt Diagnostics to Further Accelerate Pathology Digitization". PR Newswire. Cision US Inc. https://www.prnewswire.com/news-releases/vistapath-launches-new-collaboration-with-gestalt-diagnostics-to-further-accelerate-pathology-digitization-301594718.html. Retrieved 17 February 2023. 
  26. 26.0 26.1 26.2 26.3 26.4 26.5 26.6 26.7 Blum, K. (1 January 2023). "A Status Report on AI in Laboratory Medicine". Clinical Laboratory News. American Association for Clinical Chemistry. https://www.aacc.org/cln/articles/2023/janfeb/a-status-report-on-ai-in-laboratory-medicine. Retrieved 17 February 2023. 
  27. Chubb, P. (3 November 2020). "How disruptive technology is helping laboratories combat COVID-19". Datafloq. https://datafloq.com/read/disruptive-technologies-lab-help-us-prepare-future-pandemics/. Retrieved 16 February 2023. 
  28. Stewart, B. (18 March 2021). "Using LIMS for Data Visualization". CSols Insights. CSols, Inc. https://www.csolsinc.com/insights/published-articles/using-lims-for-data-visualization/. Retrieved 17 February 2023. 
  29. "Data Analytics". Scientific Computing World - Building a Smart Laboratory 2020. Europa Science Ltd. 29 January 2021. https://www.scientific-computing.com/feature/data-analytics-0. Retrieved 17 February 2023. 
  30. Optima Team (15 December 2020). "The concept of machine learning applied to lab resources scheduling". Optima Blog. Optima PLC Tracking Tools S.L. https://www.optima.life/blog/the-concept-of-machine-learning-applied-to-lab-resources-scheduling/. Retrieved 17 February 2023. 
  31. 31.0 31.1 31.2 Lee, G.F. (10 October 2022). "The Robot May See You Now: It’s time to stop and think about the ethics of artificial intelligence". The Pathologist. https://thepathologist.com/outside-the-lab/the-robot-may-see-you-now. Retrieved 17 February 2023. 
  32. 32.0 32.1 32.2 Sherwood, L. (10 February 2022). "SLAS 2022: Barriers remain to AI adoption in life sciences". LabPulse.com Showcasts. Science and Medicine Group. https://www.labpulse.com/showcasts/slas/2022/article/15300130/slas-2022-barriers-remain-to-ai-adoption-in-life-sciences. Retrieved 17 February 2023. 
  33. 33.0 33.1 33.2 33.3 Bellini, Claudia; Padoan, Andrea; Carobene, Anna; Guerranti, Roberto (25 November 2022). "A survey on Artificial Intelligence and Big Data utilisation in Italian clinical laboratories" (in en). Clinical Chemistry and Laboratory Medicine (CCLM) 60 (12): 2017–2026. doi:10.1515/cclm-2022-0680. ISSN 1434-6621. https://www.degruyter.com/document/doi/10.1515/cclm-2022-0680/html. 
  34. Chada, Bharadwaj V; Summers, Leanne (10 October 2022). "AI in the NHS: a framework for adoption" (in en). Future Healthcare Journal: fhj.2022–0068. doi:10.7861/fhj.2022-0068. ISSN 2514-6645. PMC PMC9761451. PMID 36561823. https://www.rcpjournals.org/lookup/doi/10.7861/fhj.2022-0068.