通讯作者: 张俊, 1273800868@qq.com

DOI：10.12201/bmr.202512.00069

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

• 摘要：目的 探讨人工智能（artificial intelligence，AI）辅助的以问题为基础的教学法（problem-based learning，PBL）联合以病例为基础的教学法（case-based learning，CBL）在医学影像学实习教学中的应用效果。方法 将南京医科大学康达学院40名医学影像学实习生按实习轮转顺序随机分为试验组与对照组各20人。对照组采用传统讲授式教学（lecture-based learning，LBL），试验组采用AI联合PBL-CBL教学模式。教学结束后比较两组学生的理论知识和阅片技能考核成绩，并通过问卷调查两组学生对教学模式的主观评价。结果 试验组理论知识和阅片技能成绩均高于对照组［（87.45±3.15）分 vs.（81.60±5.12）分；（33.30±2.52）分 vs.（29.20±2.29）分］，差异均有统计学意义（P ＜0.05）。试验组在自主学习能力、逻辑思维、知识掌握、临床应用能力及总体满意度等方面评分亦显著高于对照组（P ＜0.05）。结论 与传统教学方法相比，AI联合PBL-CBL教学模式可有效提升医学影像学实习教学质量，具有推广价值。

  关键词： 人工智能；医学影像学；实习教学；PBL教学法；CBL教学法; ; ; ; 

   

  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

  提交时间：2025-12-26

  版权声明：作者本人独立拥有该论文的版权，预印本系统仅拥有论文的永久保存权利。任何人未经允许不得重复使用。
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• 序号	提交日期	编号	操作
  1	2025-12-08	10.12201/bmr.202512.00069V1	下载

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