Application of Artificial Intelligence Combined with PBL-CBL Teaching Model in Medical Imaging Internship Education

Corresponding author: Zhang Jun, 1273800868@qq.com

DOI: 10.12201/bmr.202512.00069

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  Abstract: Objective To explore the application and teaching effectiveness of an artificial intelligence (AI)-assisted problem-based learning (PBL) combined with case-based learning (CBL) teaching model in medical imaging internship education. Methods Forty medical imaging interns from Kangda College of Nanjing Medical University were selected and randomly divided into an experimental group and a control group (20 students each) according to their internship rotation sequence. The control group received traditional lecture-based learning (LBL), while the experimental group received an AI-assisted PBL-CBL teaching model. After the teaching intervention, the theoretical knowledge test scores and image-interpretation skills test scores of the two groups were compared. In addition, a questionnaire survey was used to compare the students’ subjective evaluations of the teaching effectiveness of their respective teaching models. Results The experimental group achieved higher scores in both theoretical knowledge and image-interpretation skills than the control group (87.45±3.15 vs. 81.60±5.12) points; (33.30±2.52 vs. 29.20±2.29) points, and the differences were statistically significant (P<0.05). Subjective evaluations showed that the experimental group scored significantly higher than the control group in autonomous learning ability, logical thinking, knowledge mastery, clinical application ability, and overall satisfaction, with statistically significant differences (P<0.05). Conclusion Compared with traditional teaching methods, the AI-assisted PBL-CBL teaching model can effectively improve the quality of medical imaging internship education and is worthy of promotion and application in clinical teaching in radiology departments.

  Key words: artificial intelligence; medical imaging; Internship teaching; PBL; CBL

  Submit time: 26 December 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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