Pediatric radiology presents unique educational challenges, requiring trainees to master complex imaging patterns across varying patient ages and developmental stages. We developed an innovative learning platform powered by large language models (LLMs) to address these challenges through personalized, adaptive instruction. Our approach aims to bridge the gap between traditional teaching methods and the need for consistent, scalable feedback in pediatric imaging education. Read More

Meeting name: SPR 2025 Annual Meeting , 2025

Authors: Battle Wilson, Bala Wasif, Smith Hayden, Moon John, Li Hanzhou, Weinberg Brent, Trivedi Hari

Keywords: Adaptive Learning, Educational Informatics