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 Martins BMCMedInfoDecMak2017 17-1.gif|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:The effect of a test ordering software intervention on the prescription of unnecessary laboratory tests - A randomized controlled trial|The effect of a test ordering software intervention on the prescription of unnecessary laboratory tests - A randomized controlled trial]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''
The way [[electronic health record]] and [[Computerized physician order entry|laboratory test ordering system]] software is designed may influence physicians’ prescription. A randomized controlled trial was performed to measure the impact of a diagnostic and laboratory tests ordering system software modification.


Participants were family physicians working and prescribing diagnostic and [[laboratory]] tests. The intervention group had modified software with basic shortcut menu changes, where some tests were withdrawn or added, and with the implementation of an evidence-based [[clinical decision support system]] based on United States Preventive Services Task Force (USPSTF) recommendations. This intervention group was compared with typically used software (control group).
[[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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The outcomes were the number of tests prescribed from those: withdrawn from the basic menu; added to the basic menu; marked with green dots (USPSTF’s grade A and B); and marked with red dots (USPSTF’s grade D). ('''[[The effect of a test ordering software intervention on the prescription of unnecessary laboratory tests - A randomized controlled trial|Full article...]]''')<br />
{{flowlist |
<br />
* [[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]]
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* [[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:The state of open-source electronic health record projects: A software anthropology study|The state of open-source electronic health record projects: A software anthropology 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]]
: ▪ [[Journal:PCM-SABRE: A platform for benchmarking and comparing outcome prediction methods in precision cancer medicine|PCM-SABRE: A platform for benchmarking and comparing outcome prediction methods in precision cancer medicine]]
}}
: ▪ [[Journal:Ten simple rules for cultivating open science and collaborative R&D|Ten simple rules for cultivating open science and collaborative R&D]]

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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