Artificial Intelligence in the Training of Radiology Residents: a Multicenter Randomized Controlled Trial

The aim of the present study was to compare the effectiveness of AI-assisted training and conventional human training in clinical practice. This was a multicenter, randomized, controlled clinical trial conducted in five national-level residency training hospitals. Residents from five hospitals participated, divided into three groups: conventional training (Group A), conventional plus specialty training (Group B), and conventional plus AI-assisted training (Group C). The content of the training was ultrasound diagnosis of thyroid nodules. The training lasted for 18 months, and the three groups of participants were phase-tested every 3 months to compare the effect of the training. The diagnostic accuracy of all three groups gradually increased with increasing training time. Among the three groups, groups B and C had higher accuracy than group A (P < .001), and there was no significant difference between groups B and C (P = .64). Over the training period, diagnostic confidence increased in all groups. Negative activating emotions decreased significantly over time in all groups (95% CI, − 0.81 to − 0.37; P < .001), while positive activating emotions increased significantly (95% CI, 0.18 to 0.53; P < .001). Current research shows that all three approaches are viable for training radiology residents. Furthermore, the AI-assisted approach had no negative emotional impact on the trainees, suggesting that integrating AI into radiology training programs could provide a reliable and effective means of achieving the educational goals of medical education.