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:Fig3 Dundar JofPathInfo2022 13.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Anatomic pathology quality assurance: Developing an LIS-based tracking and documentation module for intradepartmental consultations|Anatomic pathology quality assurance: Developing an LIS-based tracking and documentation module for intradepartmental consultations]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


An electronic intradepartmental consultation system for [[Anatomical pathology|anatomic pathology]] (AP) was conceived and developed in the [[laboratory information system]] (LIS) of University of Iowa Hospitals and Clinics in 2019. Previously, all surgical pathology intradepartmental consultative activities were initiated and documented with paper forms, which were circulated with the pertinent microscopic slides and were eventually filed. In this study, we discuss the implementation and utilization of an electronic intradepartmental AP consultation system. [[Workflow]]s and procedures were developed to organize intradepartmental surgical pathology consultations from the beginning to the end point of the consultative activities entirely using a paperless system that resided in the LIS ... ('''[[Journal:Anatomic pathology quality assurance: Developing an LIS-based tracking and documentation module for intradepartmental consultations|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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