The role of interdisciplinary collaboration and artificial intelligence in radiology residency education

Abstract

BackgroundModern medical education demands refined methods, especially in radiology, where accuracy, speed, and clinical decision-making are critical.PurposeTo evaluate the impact of artificial intelligence (AI)-assisted and interdisciplinary educational interventions on residents' theoretical knowledge, confidence in professional skills, and practical clinical abilities. Assessments were conducted at Kirkpatrick Level 2 (Learning) for knowledge. Level 3 (Behavior) and Level 4 (Results) were not assessed in this study due to logistical constraints.Material and MethodsThe study was conducted between January and June 2024 at three medical centers in Shenzhen, China. A total of 240 residents were randomly assigned to three groups of 80 each: group 1 received standard training; group 2 participated in interdisciplinary seminars; and group 3 engaged in AI-assisted learning activities. The study included three stages: baseline assessment, core educational intervention, and final evaluation. Statistical analyses included Shapiro-Wilk and Kolmogorov-Smirnov tests for normality, followed by ANOVA and Tukey's post hoc tests for group comparisons.ResultsResidents in groups 2 and 3 demonstrated significant improvements across all measured domains. Group 3 (AI-assisted training) showed the greatest gains, with theoretical knowledge increasing by 21.5%, confidence in professional skills by 39.4%, and clinical skill performance by 27.1%. All between-group differences were statistically significant (<i>P</i> <0.01).ConclusionThe findings underscore the benefit of combining technology-driven exercises with collaborative, multispecialty learning to strengthen clinical competence. Future research should examine how such AI-based interventions influence long-term performance and how they can be adapted to different training environments.

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