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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 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, with federal removal of hemp containing no more than 0.3 percent THC from the controlled substances list.<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> However, cannabis containing more than 0.3 percent THC remains 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> 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>


===Testing and research===
*What's going on in these labs?
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>
:'''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" />
:'''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>


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/20170119191646/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">{{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> 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>
*A number of challenges exist in the realm of effectively and securely implementing AI in the laboratory. This includes:


An excerpt from the previously mentioned testimony of NIDA's Dr. Weiss summates this well:
: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" />


<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 FDA-approved) 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>
*Given those challenges, some considerations should be made about implementing AI-based components in the laboratory. Examples include:
 
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 concerns===
The U.S. cannabis industry has a few additional concerns, 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>
 
==Regulatory scheme==
 
===Federal===
 
'''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 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 Department of Justice 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>
 
===State medical and recreational===
 
 
==Laboratory testing of cannabis==
 
===Tests and standards===
 
===Reports===
 
===Lab equipment used===
 
===Software===
 
===Testing labs and pricing info===
 
 
==Future of cannabis testing and market trends==
 
 
==Resources==
 
===Trade shows===
 
===Producers and vendors===
 
===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==
{{Reflist|colwidth=30em}}
{{Reflist|colwidth=30em}}
<!--Place all category tags here-->

Latest revision as of 19:33, 17 February 2023

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 my discussion page instead.

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. 
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