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

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'''[[Clinical pathology]]''' (US, UK, Ireland, Commonwealth, Portugal, Brazil, Italy), '''laboratory medicine''' (Germany, Romania, Poland, Eastern Europe), '''clinical analysis''' (Spain), or '''clinical/medical biology''' (France, Belgium, Netherlands, Austria, North and West Africa) is a medical specialty concerned with the diagnosis of disease based on the [[laboratory]] analysis of bodily fluids, such as blood, urine, and tissues using the tools of chemistry, microbiology, hematology, and molecular pathology. Clinical pathologists work in close collaboration with clinical scientists (clinical biochemists, clinical microbiologists, etc.), medical technologists, [[hospital]] administrators, and referring physicians to ensure the accuracy and optimal utilization of laboratory testing. This specialty requires a medical residency and should not be confused with biomedical science, which is not necessarily related to medicine.
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Clinical pathology is one of two major divisions of pathology, the other being [[anatomical pathology]]. Often, pathologists practice both anatomical and clinical pathology, a combination sometimes known as general pathology. The distinction between clinical and anatomic pathology is increasingly blurred by the introduction of technologies that require new expertise and the need to provide patients and referring physicians with integrated diagnostic reports. ('''[[Clinical pathology|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'': [[Anatomical pathology]], [[Information]], [[Clinical laboratory]]
{{flowlist |
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* [[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...)
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