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

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'''[[Health informatics]]''' (also called '''health care informatics''', '''healthcare informatics''', '''medical informatics''', '''nursing informatics''',  '''clinical informatics''', or '''biomedical informatics''') is a discipline at the intersection of [[information science]], computer science, and health care. It deals with the resources, devices, and methods required to optimize the "collection, storage, retrieval, [and] communication ... of health-related data, [[information]], and knowledge." Health informatics is applied to the areas of nursing, clinical care, dentistry, pharmacy, public health, occupational therapy, and biomedical research. Health informatics resources include not only computers but also clinical guidelines, formal medical terminologies, and information and communication systems.
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Worldwide use of technology in medicine began in the early 1950s with the rise of computers. Medical informatics research units began to appear during the 1970s in Poland and in the U.S., with medical informatics conferences springing up as early as 1974. Since then the development of high-quality health informatics research, education, and infrastructure has been the goal of the U.S. and the European Union. Hundreds of datasets, publications, guidelines, specifications, meetings, conferences, and organizations around the world continue to shape what health informatics is today. ('''[[Health informatics |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'': [[Content delivery network]], [[Federally qualified health center]], [[Home health agency]]
{{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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