Difference between revisions of "Template:Article of the week"

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A '''[[rural health clinic]]''' ('''RHC''') is a special facility designation of the U.S. [[Centers for Medicare and Medicaid Services]] (CMS), defined as a clinic in a non-urbanized area designated by the Health Resources and Services Administration as being in a health professional shortage or medically underserved area.
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


In September 1999, nearly 3,500 RHCs were operating across 45 states. By January 2013, that number rose to nearly 3,800. RHCs were established by the Rural Health Clinic Services Act of 1977, otherwise known as Public Law 95-210. The program was established to address an inadequate supply of physicians serving Medicare beneficiaries and Medicaid recipients in rural areas and to increase the utilization of non-physician practitioners. To qualify as an RHC the facility must be located in a non-urban area, as described by the United States Census Bureau, and must be defined as being in a medically underserved area by one of several possibilities. ('''[[Rural health clinic|Full article...]]''')<br />
[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and [[Data visualization|visualize]] large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in [[Information management|data management]] that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of [[machine learning]] (ML), which holds much promise for this domain ... ('''[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'': [[Picture archiving and communication system]], [[Cytopathology]], [[Clinical pathology]]
{{flowlist |
* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]
* [[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]
* [[Journal:GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration|GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration]]
}}

Revision as of 17:50, 15 April 2024

Tab1 Williamson F1000Res2023 10.png

"Data management challenges for artificial intelligence in plant and agricultural research"

Artificial intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and visualize large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of machine learning (ML), which holds much promise for this domain ... (Full article...)
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