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

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'''"[[Journal:A review of the role of public health informatics in healthcare|A review of the role of public health informatics in healthcare]]"'''
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Recognized as information intensive, healthcare requires timely, accurate information from many different sources generated by health information systems (HIS). With the availability of information technology in today's world and its integration in healthcare systems, the term “[[public health informatics]] (PHI)” was coined and used. The main focus of PHI is the use of information science and technology for promoting population health rather than individual health. PHI has a disease prevention rather than treatment focus in order to prevent a chain of events that leads to a disease's spread. Moreover, PHI often operates at the government level rather than in the private sector. This review article provides an overview of the field of PHI and compares paper-based surveillance system and public health information networks (PHIN). The current trends and future challenges of applying PHI systems in the Kingdom of Saudi Arabia (KSA) were also reported. ('''[[Journal:A review of the role of public health informatics in healthcare|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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''Recently featured'':
''Recently featured'':  
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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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