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.

Publish Date

Oct 1, 2025

Topic

  • Artificial Intelligence
  • Machine Learning
  • Radiology
  • Research

Resource Type

  • JIIM

Audience Type

  • Clinician
  • Developer
  • Imaging IT
  • Researcher/Scientist
  • Student Member in Training (SMIT)
  • Vendor

Visit This Resource

Related Resources

Resource

Multi-Agent AI and Ethics in Radiology: Navigating the Trust Crisis in Advanced Medical Systems
(Part 1)

Sep 9, 2025

Part 1: The Medical AI Background: From Simple Tools to Complex Systems Picture this: you’re a radiologist examining a chest…

Resource

Multi-Agent AI and Ethics in Radiology: Navigating the Trust Crisis in Advanced Medical Systems
(Part 2)

Sep 9, 2025

Part 2: Framework, Regulation & Future Directions Additions Ethical Considerations in Medical AI Deployment The deployment of multi-agent AI systems…

Resource

Liquid Foundation Models: Revolutionizing AI Adaptability and Efficiency

Aug 26, 2025

Introduction In the rapidly evolving landscape of artificial intelligence, a new paradigm has emerged that promises to address some of…