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

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab2 Matthews JPathInfo2017 8.jpg|240px]]</div>
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'''"[[Journal:Usability evaluation of laboratory information systems|Usability evaluation of laboratory information systems]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Numerous studies have revealed widespread clinician frustration with the usability of [[electronic health record]]s (EHRs) that is counterproductive to adoption of EHR systems to meet the aims of healthcare reform. With poor system usability comes increased risk of negative unintended consequences. Usability issues could lead to user error and workarounds that have the potential to compromise patient safety and negatively impact the quality of care. While there is ample research on EHR usability, there is little [[information]] on the usability of [[laboratory information system]]s (LIS). Yet, an LIS facilitates the timely provision of a great deal of the information needed by physicians to make patient care decisions. Medical and technical advances in genomics that require processing of an increased volume of complex [[laboratory]] data further underscore the importance of developing a user-friendly LIS. This study aims to add to the body of knowledge on LIS usability. ('''[[Journal:Usability evaluation of laboratory information systems|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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