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

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'''"[[Journal:Emerging cybersecurity threats in radiation oncology|Emerging cybersecurity threats in radiation oncology]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Modern image-guided radiation therapy is dependent on information technology and data storage applications that, like any other digital technology, are at risk from cyberattacks. Owing to a recent escalation in cyberattacks affecting radiation therapy treatments, the American Society for Radiation Oncology's ''Advances in Radiation Oncology'' is inaugurating a new special manuscript category devoted to [[cybersecurity]] issues. We conducted a review of emerging cybersecurity threats and a literature review of cyberattacks that affected [[Radiation oncologist|radiation oncology]] practices. In the last 10 years, numerous attacks have led to an interruption of radiation therapy for thousands of patients, and some of these catastrophic incidents have been described as being worse than [[coronavirus disease 2019]]'s impact on healthcare centers in New Zealand ... ('''[[Journal:Emerging cybersecurity threats in radiation oncology|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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