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

Abstract

Background

Modern medical education demands refined methods, especially in radiology, where accuracy, speed, and clinical decision-making are critical.

Purpose

To 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 Methods

The 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.

Results

Residents 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 (P <0.01).

Conclusion

The 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.