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

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'''"[[Journal:Strategies for laboratory professionals to drive laboratory stewardship|Strategies for laboratory professionals to drive laboratory stewardship]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Appropriate [[laboratory]] [[Medical test|testing]] is critical in today's healthcare environment that aims to improve patient care while reducing cost. In recent years, laboratory stewardship has emerged as a strategy for assuring [[Quality (business)|quality]] in laboratory medicine with the goal of providing the right test for the right patient at the right time. Implementing a laboratory stewardship program now presents a valuable opportunity for laboratory professionals to exercise leadership within health systems and to drive change toward realizing aims in healthcare. The proposed framework for program implementation includes five key elements: 1) a clear vision and organizational alignment; 2) appropriate skills for program execution and management; 3) resources to support the program; 4) incentives to motivate participation; and, 5) a plan of action that articulates program objectives and metrics. This framework builds upon principles of [[change management]], with emphasis on engagement with clinical and administrative stakeholders and the use of clinical data as the basis for change. These strategies enable laboratory professionals to cultivate organizational support for improving laboratory use and take a leading role in providing high-quality patient care. ('''[[Journal:Strategies for laboratory professionals to drive laboratory stewardship|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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