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Teaching AI for Radiology Applications: A Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM
In a groundbreaking collaboration led by the SIIM Machine Learning Education Subcommittee, the American Association of Physicists in Medicine (AAPM), Radiological Society of North America (RSNA), and Society for Imaging Informatics in Medicine (SIIM) are proud to announce the simultaneous co-publication of “Teaching AI for Radiology Applications: A Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM” in these three flagship journals: Medical Physics, Radiology: Artificial Intelligence, and the Journal of Imaging Informatics in Medicine (JIIM).
The society-endorsed syllabus outlines critical competencies for four key groups: clinical users, technology purchasers, clinical collaborators, and AI developers. Designed as a flexible framework, it allows institutions to adapt content while ensuring consistent instruction on AI fundamentals, clinical integration, regulatory requirements, and ethical considerations.
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Multi-Agent AI and Ethics in Radiology: Navigating the Trust Crisis in Advanced Medical Systems
(Part 1)
Part 1: The Medical AI Background: From Simple Tools to Complex Systems Picture this: you’re a radiologist examining a chest…
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Multi-Agent AI and Ethics in Radiology: Navigating the Trust Crisis in Advanced Medical Systems
(Part 2)
Part 2: Framework, Regulation & Future Directions Additions Ethical Considerations in Medical AI Deployment The deployment of multi-agent AI systems…