Teaching AI for Radiology Applications: A Multisociety‑Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM

Area of Science:

• Medical imaging
• Machine learning
• Artificial intelligence

Background:

• Medical imaging is rapidly advancing due to machine learning (ML) and increased computational power.
• ML algorithms offer potential improvements in disease detection, diagnosis, prognosis, and treatment.
• Challenges include ML model complexity, data requirements, and potential for bias, hindering safe adoption.

Purpose of the Study:

• To address challenges in adopting ML in medical imaging.
• To develop a competency syllabus for medical imaging professionals interacting with AI systems.
• To guide users, purchasers, clinical collaborators, and developers of AI systems.

Main Methods:

• Collaboration between major medical imaging professional societies (AAPM, ACR, RSNA, SIIM).
• Development of a syllabus outlining recommended competencies.
• Focus on task-specific demands for different professional roles.

Main Results:

• A syllabus detailing competencies for medical imaging professionals engaging with AI.
• The syllabus targets four key personas: users, purchasers, clinical collaborators, and developers.
• The framework emphasizes safe and effective utilization of ML systems.

Conclusions:

• The competency syllabus aims to enhance the safe and effective use of ML in medical imaging.
• By addressing role-specific needs, the guide supports improved patient care.
• This syllabus provides a foundational framework, not a prescriptive curriculum.