A Framework for the Future of Imaging AI Education
As AI rapidly transforms medical imaging, the need for well-structured, multidisciplinary education has become increasingly urgent. This syllabus provides a society-endorsed framework that 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
Designed as a syllabus–not a curriculum, this flexible structure allows educators and institutions to adapt content to specific learning environments while ensuring consistent, high-quality instruction on AI fundamentals, clinical integration, regulatory issues, and ethical considerations.
“The Syllabus is a crucial checklist for users, purchasers, clinicians and developers of AI,” said Maryellen Giger, PhD, principal investigator in the MIDRC initiative on behalf of the AAPM. “It will expand as the field of AI in Radiology evolves.”
“We must educate and equip radiology professionals to help them harness AI tools that will serve an increasing role in helping radiologists and their teams provide better, more efficient patient care,” said Christoph Wald, MD, PhD, vice chair of the ACR Board of Chancellors and chair of the ACR Commission on Informatics. “By segmenting competencies according to common institutional roles—clinical users to purchasers to developers—this practical AI syllabus can help all radiology practices effectively upskill their workforce for safe, effective AI implementation.”
“In a new field like AI, it’s often difficult to know where to start in identifying what you need to learn,” said John Mongan, MD, PhD, chair of the RSNA AI Committee. “This syllabus addresses that need by providing an expert consensus role-specific roadmap to what people working with AI should know. Use of the syllabus will help to eliminate gaps in knowledge and skills, increasing the safety and effectiveness of AI in radiology.”
“It has been a privilege to contribute to a project that brought together leaders from across the radiology, informatics, and physics communities to define what AI literacy should look like for our field,” said Felipe Kitamura, MD, PhD, MS, chair, SIIM Machine Learning Committee. “Beyond the syllabus itself, this effort represents a rare consensus across disciplines and societies, providing a shared foundation that we believe could help make imaging AI safer, fairer, and genuinely useful for patients,” he added.
Empowering the Radiology Community
This joint publication highlights a unified commitment by leading societies to ensure the radiology community is well-equipped to safely and effectively navigate the AI landscape. By fostering a shared understanding of roles and responsibilities, the syllabus is expected to serve as a foundation for academic programs, residency training, continuing education, and institutional deployment strategies.
For More Information
The full syllabus is available now in the following journals: