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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Doctor reviewing pdq.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
A '''medical practice management system''' (also '''practice management system''' or '''PMS''') is a software-based information and enterprise management tool for physician offices that offers a set of key features that support an individual or group medical practice's operations. Those key features include — but are not limited to — appointment scheduling, patient registration, procedure posting, insurance billing, patient billing, payment posting, data and file maintenance, and reporting.
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The PMS has traditionally been a stand-alone application, installed on computers in the physician office. But like [[laboratory information management systems]], [[hospital information systems]], and other informatics software, trends have shifted to both web-based and cloud-based access to PMS applications. Cloud-based PMSs have been around at least since 2011, and they have become more attractive for several reasons, including the ease of letting the vendor maintain and update the technology from their end, the need for less hardware, and the convenience of accessing the system from anywhere. ('''[[Medical practice management system|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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