Advancing Radiology Education with Artificial Intelligence: Curriculum Planning, Implementation, and Evaluation

Artificial intelligence (AI) is rapidly integrating into clinical radiology, creating a continuous emphasis on the necessity of teaching its principles to radiology trainees. However, the potential of AI to transform radiology education remains underexplored. The authors review how AI can be leveraged to enhance radiology education, from curriculum planning to its implementation and evaluation. Guided by Harden's 10-step framework for curriculum development, they systematically examine current and potential future applications of AI at each stage. They detail how AI, particularly generative models, can augment traditional educational methods. Such applications include automating needs assessments through natural language processing of learner feedback, personalizing learning pathways based on performance data, and generating diverse educational content, including synthetic imaging cases and radiology board-style questions. Furthermore, AI can enhance teaching strategies through immersive simulations, streamlining assessments with objective report-comparison tools, and improving program management by automating administrative tasks. Although the potential is immense, significant limitations persist, including high implementation costs, the rapid pace of technological change, risks of AI bias and error, and concerns around data privacy. Despite these challenges, AI represents a paradigm shift for medical education. Radiology programs should pursue a strategic and pragmatic approach to AI adoption, starting with low-risk applications to build institutional capacity and prepare the next generation of radiologists for an AI-integrated future. ^©RSNA, 2026 Supplemental material is available for this article. See the invited commentary by Tejani and Cook in this issue.