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

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'''[[Health information technology]] (HIT)''' is the application of "hardware and software in an effort to manage and manipulate health data and information." HIT acts as a framework for the comprehensive management of health information originating from consumers, providers, governments, and insurers in order to improve the overall state of health care. Among those improvements, the Congressional Budget Office (CBO) of the United States believes HIT can reduce or eliminate errors from medical transcription, reduce the number of diagnostic tests that get duplicated, and improve patient outcomes and service efficiency among other things.
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The "technology" of "health information technology" represents computers, software, and communications infrastructure that can be networked to create systems for manipulating health information. As such, the science of [[Informatics (academic field)|informatics]] and its focus on information processing and systems engineering is also integral to the development, application, and evaluation of HIT. In particular the subdivision of [[health informatics]], which focuses on the resources, devices, and methods required for optimizing the acquisition, storage, retrieval, and use of information in health and biomedicine, is most relevant. However, other subdivisions of informatics such as [[medical informatics]], [[public health informatics]], [[pharmacoinformatics]], and [[translational research informatics]] are able to inform health informatics from different disciplinary perspectives. ('''[[Health information technology|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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{{flowlist |
''Recently featured'': [[Clinical decision support system]], [[Medical practice management system]], [[Biodiversity informatics]]
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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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