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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:PACS-RIS Services.png|260px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
A [[picture archiving and communication system]] (PACS) is a digital imaging system composed of a set of components that allow for the digital acquisition, archiving, communication, retrieval, processing, distribution, and display of medical images. The PACS may consist of only a few components or be sufficiently complex to handle a hospital or healthcare enterprise environment. Regardless, it must be durable enough for daily use in a clinical environment, integrate to and from several [[Imaging informatics#Diagnostic imaging modalities|medical imaging modalities]], and have sufficient workstations for technicians utilizing those modalities to perform their work inside and outside the radiology department.[1] PACS benefit healthcare providers by digitally managing medical images, eliminating the need to manually file, retrieve, or transport film jackets. This often saves processing time in both the diagnostics and reporting related to the imagery, especially when integrated with speech recognition technology. ('''[[Picture archiving and communication system|Full article...]]''')<br />
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''
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''Recently featured'': [[Cytopathology]], [[Clinical pathology]], [[Anatomical pathology]]
[[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 />
''Recently featured'':
{{flowlist |
* [[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]]
* [[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: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]]
}}

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...)
Recently featured:

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