Objective To investigate the effectiveness of artificial intelligence (AI)-assisted diagnostic tools in clerkship teaching of medical imaging for undergraduate students.

Methods Undergraduate clinical medical students who interned in the Department of Radiology of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from March to April 2025 were enrolled and randomly assigned to an AI group and a control group. The AI group received teaching supported by a picture archiving and communication system (PACS) and its integrated AI-assisted diagnostic system. In contrast, the control group received conventional lecture-based teaching with PowerPoint slides plus routine film-reading guidance. Teaching effectiveness was evaluated using the end-of-clerkship assessment, a theoretical knowledge test in medical imaging, and a teaching feedback questionnaire.

Results Ninety-nine students were included (49 in the AI group and 50 in the control group). The AI group achieved higher scores on the written examination and higher total scores on the medical imaging theory test than the control group (P < 0.05). The teaching feedback questionnaire indicated that the AI group scored higher in satisfaction with teaching methods, teaching content, and classroom activities, and performed significantly better than the control group in knowledge acquisition, learning interest, clinical thinking, and self-directed learning ability (P < 0.05).

Conclusion Integrating AI-assisted diagnostic tools into clerkship teaching of medical imaging for undergraduate students can improve learning outcomes and learning experience, promote a shift from passive observation to active operation and self-directed learning, and has potential value for wider implementation.

1. FarmakisSG, ChertoffJD, StrausCM, et al. Perspective: mandatory radiology education for medical students[J]. Acad Radiol, 2023, 30(7): 1500-1510. doi：10.1016/j.acra.2022.10.023

2. 司丽芳, 蒋涛, 刘小娟, 等. 临床专业本科生医学影像学见习课教学模式实践[J]. 中国病案, 2020, 21(7): 78-81.SiLF, JiangT, LiuXJ, et al. The teaching mode practice of medical imaging probation course in undergraduates major in clinical medicine[J]. Chinese Medical Record, 2020, 21(7): 78-81.

3. WadeSWT, VelanGM, TedlaN, et al. What works in radiology education for medical students: a systematic review and Meta-analysis[J]. BMC Med Educ, 2024, 24(1): 51. doi：10.1186/s12909-023-04981-z

4. 周俊林, 萧毅, 张雪君, 等. 中国医学影像人工智能应用现状调研报告[J]. 中华放射学杂志, 2022, 56(11): 1248-1253. doi：10.3760/cma.j.cn112149-20220802-00650ZhouJL, XiaoY, ZhangXJ, et al. A survey report on the application status of artificial intelligence in medical imaging in China[J]. Chinese Journal of Radiology, 2022, 56(11): 1248-1253. doi：10.3760/cma.j.cn112149-20220802-00650

5. ChengCT, ChenCC, FuCY, et al. Artificial intelligence-based education assists medical students' interpretation of hip fracture[J]. Insights Imaging, 2020, 11(1): 119. doi：10.1186/s13244-020-00932-0

6. 孔庆聪, 唐文洁, 樊浩, 等. 多元化PACS教学融合人工智能在放射科住培教学中的应用[J]. 临床医学研究与实践, 2025, 10(9): 175-178.KongQC, TangWJ, FanH, et al. Application of diversified PACS teaching and artificial intelligence in standardized training of residents physicians in radiology department[J]. Clinical Research and Practice, 2025, 10(9): 175-178.

7. 王建辉, 方靖, 刘有云, 等. 多元化教学模式在医学影像学本科生实习教学中的应用研究[J]. 临床放射学杂志, 2024, 43(7): 1230-1233.WangJH, FangJ, LiuYY, et al. Research on the application of diversified teaching modes in the practical teaching of undergraduate students of medical imaging[J]. Journal of Clinical Radiology, 2024, 43(7): 1230-1233.

8. AltunI, TuranO, AwanO. Revolutionizing radiology education: exploring innovative teaching methods[J]. Abdom Radiol (NY), 2025, 50(12): 6225-6234. doi：10.1007/s00261-025-05010-x

9. 赵莹, 彭春花, 叶红, 等. 人工智能驱动下中医药高等院校数学实验与案例建模教学的实践与创新[J]. 数理医药学杂志, 2025, 38(8): 636-641.ZhaoY, PengCH, YeH, et al. Practice and innovation of mathematics experiment and case modeling teaching in traditional Chinese medicine colleges and universities driven by artificial intelligence[J]. Journal of Mathematical Medicine, 2025, 38(8): 636-641.

10. 朱姬莹, 励杨晟, 朱湘文, 等. SPARK影像教学案例库在医学影像学专业本科生实习中的应用[J]. 中华全科医学, 2024, 22(8): 1416-1419.ZhuJY, LiYS, ZhuXW, et al. Application of SPARK image teaching case library in the clinical practice of undergraduate students in medical imaging[J]. Chinese Journal of General Practice, 2024, 22(8): 1416-1419.

11. 陈冲, 夏黎明. 积极稳妥地推进人工智能在医学影像的应用[J]. 中华放射学杂志, 2022, 56(1): 5-8.ChenC, XiaLM. Promoting the application of artificial intelligence in medical imaging actively and steadily[J]. Chinese Journal of Radiology, 2022, 56(1): 5-8.

12. 韩文静, 董建鑫, 周辰, 等. 人工智能背景下医学影像技术专业线性代数课程教学改革与实践[J]. 数理医药学杂志, 2024, 37(10): 799-805. doi：10.12173/j.issn.1004-4337.202409025HanWJ, DongJX, ZhouC, et al. Teaching reform and practice of the linear algebra course for medical imaging technology major under the background of artificial intelligence[J]. Journal of Mathematical Medicine, 2024, 37(10): 799-805. doi：10.12173/j.issn.1004-4337.202409025

13. 邹雪雪, 张强, 许昌, 等. BOPPPS联合AI教学模式在乳腺X线影像诊断教学中的应用[J]. 临床放射学杂志, 2025, 44(3): 570-572.ZouXX, ZhangQ, XuC, et al. Application of BOPPPS combined with AI teaching mode in mammographic diagnosis teaching[J]. Journal of Clinical Radiology, 2025, 44(3): 570-572.

14. 郭兴, 张利华, 姬军军, 等. 基于云图片存储与传输系统线上实训提升医学影像学实践教学效果的探索[J]. 实用放射学杂志, 2022, 38(8): 1383-1385.GuoX, ZhangLH, JiJJ, et al. Exploration of improving the effect of medical imaging practice teaching based on cloud picture archiving and communication system online training[J]. Journal of Practical Radiology, 2022, 38(8): 1383-1385.

15. 陈虹旭, 赖睿, 徐建国, 等. 人工智能赋能神经外科教学:机遇、挑战与对策[J]. 华西医学, 2024, 39(9): 1470-1473.ChenHX, LaiR, XuJG, et al. Artificial intelligence-enabled neurosurgery teaching: opportunities, challenges, and countermeasures[J]. West China Medical Journal, 2024, 39(9): 1470-1473.

16. de Margerie-MellonC. Leveraging artificial intelligence in radiology education: challenges and opportunities[J]. Eur Radiol, 2023, 33(11): 8239-8240. doi：10.1007/s00330-023-10112-5

17. 陈冲, 陈俊, 夏黎明. 人工智能促进医学影像临床应用与研究[J]. 放射学实践, 2024, 39(1): 12-16.ChenC, ChenJ, XiaLM. Artificial intelligence promotes clinical application and research of medical imaging[J]. Radiologic Practice, 2024, 39(1): 12-16.

18. LiMD, LittleBP. Appropriate reliance on artificial intelligence in radiology education[J]. J Am Coll Radiol, 2023, 20(11): 1126-1130. doi：10.1016/j.jacr.2023.04.019