A new study showed that using artificial intelligence to track which pathologies radiology residents saw each day made it possible to identify gaps in their clinical exposure and provide supplemental, targeted teaching cases. This approach helped ensure that residents were exposed to the full breadth of pathology needed for training, the study authors said.
Led by NYU Langone Health researchers, the study addressed a longstanding challenge in radiology education: residents traditionally learn to diagnose disease based on the real patient cases they encounter during their assigned clinical workdays, which may not cover the full range of important conditions. By monitoring daily case exposure with AI and adding targeted teaching cases, the researchers improved the breadth of pathology seen by residents without reducing their experience with real patient cases.
Publishing online recently in Academic Radiology, the team's work found that the AI could identify disease exposure gaps for radiology residents and suggest the specific patient case pathologies they need to see with more than 90 percent accuracy.
"This represents a fundamental shift in how we train radiologists—moving from a one-size-fits-all model to one that automatically addresses each resident's specific learning needs," said Michael P. Recht, MD, chair of the Department of Radiology at NYU Langone. "No other system provides this level of personalized, data-driven training where residents work, and we think this tool will be much sought after in the field."