The American Association of Physicists in Medicine (AAPM), the American College of Radiology (ACR), RSNA, and the Society for Imaging Informatics in Medicine (SIIM) have published a framework in their respective flagship journals for educating radiologists about AI.
The publication, “Teaching AI for Radiology Applications: A Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM,” is designed as a syllabus and outlines critical competencies for four key stakeholder groups:
Users who apply AI in clinical workflows
Purchasers who evaluate and acquire AI technologies
Clinical collaborators who guide development with domain expertise
Developers who build algorithms for real-world deployment
The syllabus offers a flexible structure that allows educators and institutions to adapt content to specific learning environments while ensuring consistent instruction on AI fundamentals, clinical integration, regulatory issues, and ethical considerations, the societies said in a joint news release.
The collaboration was led by SIIM’s Machine Learning Education Subcommittee. The full, open-access publication is available now in the journals Medical Physics, Radiology: Artificial Intelligence, and the Journal of Imaging Informatics in Medicine.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
