Artificial Intelligence-Enabled Precision Education: A Novel Tool to Augment Radiology Residency Training

Radiology residency often fails to account for individual differences between residents or provide sufficient exposure to diverse pathologies. We sought to evaluate whether artificial intelligence (AI)-enabled "Precision Education" can accurately identify and address individual radiology resident pathology exposure gaps through supplemental personalized teaching cases.

A curriculum outlined types and frequencies of important pathologies (IPs) residents should encounter during postgraduate years 2 through 4 (PGY-2 through PGY-4). Daily resident "live" clinical reports were analyzed by ChatGPT-4o prompts to detect IPs encountered. Each resident's live cases were then supplemented with curated anonymized teaching cases, with priority given to IPs encountered below curriculum-defined target thresholds to date. Volumes and IP exposure were compared between pre- (2022-2023) and postintervention (2024-2025) academic years.

ChatGPT-4o demonstrated over 91% precision and recall in accurately identifying IPs. Unique IPs encountered by residents significantly increased postintervention from median 75-107 to 93.5-144 in abdominal, 43.5-70 to 73-99 in musculoskeletal, 32.5-38 to 64.5-79 in neuro-, 39.5-49 to 82-96.5 in pediatric, and 42.5-56 to 49.3-85.3 in thoracic imaging (all p < 0.05). Residents met significantly more curriculum-defined targets postintervention, increasing from a median 54-64 to 78.5-131.5 in abdominal, 21-49 to 51.5-75.5 in musculoskeletal, 3.5-9 to 21-38 in neuro-, 13.5-21 to 51.5-72 in pediatric, and 12.5-33 to 23-58 in thoracic imaging (all p < 0.05). Median live case interpretations were not significantly reduced by the intervention, aside from PGY-3 abdominal imaging cases (p = 0.0489).

Personalized AI-enabled Precision Education accurately identified resident pathology exposure gaps, enhanced exposure to IPs, and maintained clinical training opportunities.