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

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'''"[[Journal:Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing|Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Point-of-care testing (POCT) is one of the fastest growing disciplines in [[clinical laboratory]] medicine. POCT [[Medical device|devices]] are widely used in both acute and chronic patient management in the [[hospital]] and [[Physician office laboratory|primary care physician office]] settings. As demands for POCT in various healthcare settings increase, managing POCT testing quality and [[regulatory compliance]] are continually challenging. Despite technological advances in applying automatic system checks and built-in [[quality control]] to prevent analytical and operator errors, poor planning for POCT [[Interface (computing)|connectivity]] and [[Informatics (academic field)|informatics]] can limit [[Data sharing|data accessibility]] and [[Information management|management]] efficiency which impedes the utilization of POCT to its full potential. This article will summarize how connectivity and data management systems can improve timely access to POCT results, effective management of POCT programs, and ensure regulatory compliance. ('''[[Journal:Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing|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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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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