A Multi-Level Perspective on Generative AI Usage, Supervision, and Policy in Radiology Training and Education.
Authors
Affiliations (5)
Affiliations (5)
- Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106 (R.G.).
- Cleveland Clinic Lerner College of Medicine, 9500 Euclid Ave, Cleveland, OH, 44195 (K.S., P-H.C.).
- Diagnostics Institute, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH, 44195 (B.H., P-H.C.).
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104 (M.S.).
- Cleveland Clinic Lerner College of Medicine, 9500 Euclid Ave, Cleveland, OH, 44195 (K.S., P-H.C.); Diagnostics Institute, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH, 44195 (B.H., P-H.C.). Electronic address: [email protected].
Abstract
The rapid integration of generative artificial intelligence (AI) into medical education has outpaced formal guidance, creating a "hidden curriculum" for learners. This multi-level perspective convenes a panel across the academic radiology continuum, from a pre-medical student to a radiology residency program director, to examine how AI is currently reshaping training. While trainees actively use generative AI models for clinical translation and study efficiency, limited institutional guidance exists for appropriate use, risking data bias and cognitive deskilling. This perspective advocates for an "AI-conscious" teaching approach, as opposed to prohibitive policies, embedding formal AI education in the medical training pipeline to prepare the next generation of radiologists.