Hands-on Artificial Intelligence Education for Radiology Residents: A Three-year Feasibility and Curriculum Implementation Study.

RATIONALE AND OBJECTIVES: Artificial intelligence (AI) has rapidly transformed radiology practice, yet structured and practical AI education remains inconsistently integrated into radiology residency training. We developed and implemented a hands-on AI curriculum designed to integrate core computational principles with clinically relevant imaging applications. This study describes the curriculum design and evaluates its feasibility, reproducibility, and preliminary educational outcomes over three consecutive years of implementation. MATERIALS AND METHODS: An 8-hour interactive AI rotation was incorporated into the diagnostic radiology residency curriculum at a single academic institution from 2023 through 2025. The curriculum combined brief didactic instruction with structured laboratory modules covering convolution, radiomics, machine learning, deep learning, model evaluation, bias, and case-based applications. Each year, a new cohort completed the curriculum. Cohort sizes were 9 residents in 2023, 8 in 2024, and 10 in 2025 (total N=27). As an educational innovation initiative, evaluation focused on post-rotation knowledge demonstration and qualitative feedback rather than pre-post comparison. Post-rotation knowledge was assessed with written quizzes covering core curriculum domains, and learner perceptions were subsequently evaluated using a 9-item, 5-point Likert-scale survey (1 = strongly disagree, 5 = strongly agree) administered to residents who had completed the curriculum, supplemented by free-text feedback. RESULTS: Across three consecutive cohorts (N=27), all residents completed the rotation and post-rotation assessment, and curricular structure and duration remained consistent across cohorts, supporting feasibility and reproducibility. Estimated aggregate post-training assessment performance was approximately 80-90% correct responses, though individual scores were not centrally archived and this percentage should be interpreted as an approximate, retrospective estimate rather than validated outcome data. A survey administered to residents who had completed the curriculum (16 of 27 eligible residents responded) showed mixed to favorable perceptions across nine domains. Ratings were most positive for overall value (62% agree/strongly agree versus 19% disagree/strongly disagree) and awareness of AI limitations (62% versus 12%), but split closer to evenly for several other domains, including appropriateness of technical complexity, where 50% of respondents disagreed that complexity matched their training level. Free-text feedback consistently recommended a more introductory primer for learners without prior AI/coding exposure and greater emphasis on clinical application. CONCLUSION: A structured, hands-on AI curriculum integrated into residency training is feasible and sustainable across independent cohorts, with post-training assessment performance and mixed-to-favorable learner survey ratings suggesting attainment of foundational AI competencies. Learner feedback also identified specific opportunities for improvement, including greater introductory scaffolding and clinical context. Experiential learning may help bridge theoretical AI principles and clinical imaging practice. Multi-institutional and prospectively validated studies are needed before broader or scalable adoption can be recommended.