Original Title: Teaching AI for Radiology Applications: A Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM
Journal: Radiology. Artificial intelligence
DOI: 10.1148/ryai.250137
Overview
This paper presents a recommended syllabus for artificial intelligence (AI) education in radiology, developed through a collaboration of four major U.S. societies: 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). The framework addresses the growing need for standardized competencies as AI tools become more common in medical imaging. It defines the required knowledge for four distinct professional roles, or “personas”: users of AI systems (e.g., radiologists), purchasers (e.g., hospital administrators), physician collaborators who provide clinical expertise during AI development, and developers of AI systems. The document is intentionally a syllabus, outlining key topics, rather than a prescriptive curriculum, to allow educational institutions flexibility in how they teach the material. The ultimate goal is to equip all stakeholders to develop, deploy, and use AI systems safely and effectively.
Novelty
The paper’s primary contribution is its collaborative, role-based approach to standardizing AI education in medical imaging. Rather than proposing a single, uniform curriculum, it tailors a syllabus to four specific professional personas, acknowledging that different stakeholders require distinct knowledge to interact with AI responsibly. For example, a clinical user must understand an AI tool's limitations and failure modes, whereas a purchaser needs to evaluate its return on investment and workflow integration. This structured, multi-perspective framework is a significant step forward from prior, more fragmented educational efforts. Furthermore, the joint endorsement by four leading professional societies gives the syllabus substantial authority and promotes a unified standard across the field. This consensus-driven foundation provides a clear, organized guide for developing targeted and relevant AI training programs for the entire medical imaging community.
Potential Clinical / Research Applications
The direct application of this syllabus is in the creation of educational materials for radiology professionals. Medical schools, residency programs, and continuing medical education providers can use this framework to design curricula, workshops, and certification programs specific to the four defined roles. Healthcare institutions can adopt it to structure internal training for staff who will use or manage AI systems, ensuring a baseline competency for safe deployment. For AI companies, the syllabus offers a clear guide to the clinical context, workflow considerations, and regulatory landscape, which could help in the development of more clinically relevant tools. In research, this standardized framework could be used to assess the impact of targeted AI education on clinical outcomes, user adoption rates, and the frequency of AI-related errors, providing evidence to further refine future training standards.
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