Four leading radiology organizations — the American Association of Physicists in Medicine (AAPM), the American College of Radiology (ACR), the Radiological Society of North America (RSNA), and the Society for Imaging Informatics in Medicine (SIIM) — have jointly developed a new syllabus designed to enhance AI literacy across the medical imaging community.
Titled ‘Teaching AI for Radiology Applications: A Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM’, the publication has been simultaneously released in Medical Physics (AAPM), Radiology: Artificial Intelligence (RSNA), and the Journal of Imaging Informatics in Medicine (JIIM). The framework, created under the SIIM Machine Learning Education Subcommittee, outlines essential competencies for users, purchasers, clinical collaborators, and developers of AI tools in radiology.
The syllabus provides a flexible structure for integrating AI education into clinical and academic settings, addressing areas such as clinical integration, regulatory issues, and ethical considerations. It aims to establish a shared foundation for consistent, high-quality instruction while allowing educators to tailor content to specific institutional needs.
This collaborative effort reflects a unified approach by leading societies to ensure safe and effective AI implementation in radiology practice. The syllabus is available through the participating journals’ websites.
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