Artificial Intelligence in Radiology Residency Training

As artificial intelligence becomes increasingly embedded in radiology workflows, residency programs face growing pressure to prepare trainees not only to use AI tools, but to critically evaluate them. Future radiologists need a practical understanding of model performance, output interpretation, bias, validation, and clinical limitations in order to make informed decisions in AI-enabled environments. 

This session examines how radiology education can evolve to build meaningful AI literacy without sacrificing core diagnostic training. Attendees will explore curriculum models, dedicated AI learning experiences, and strategies for integrating foundational informatics, ethics, and multidisciplinary collaboration into residency education. Discussion will also address which traditional training tasks may need to shift as automation changes image acquisition, interpretation, and reporting. 

Participants will leave with practical approaches for preparing residents to serve as informed clinical partners in the selection, assessment, and responsible use of AI technologies across radiology practice.  

Objectives 

• Describe the core AI competencies radiology residents need to safely evaluate and use AI tools in clinical practice. 
• Compare educational models for integrating AI literacy, informatics, and ethics into radiology residency curricula. 
• Identify strategies for balancing foundational radiology training with emerging AI-driven workflow changes.