Large language models in aiding course design for standardized residency training: a case study on teaching the interpretation of chest X-ray plain film reports

Zhong Jingyu; Xing Yue; Hu Yangfan; Min Qinghua; Zhu Caisong; Shi Dandan; Fan Xiaoyu; Chu Jingshen; Zhang Huan; Yao Weiwu

doi:10.3760/cma.j.cn116021-20240922-01963

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Large language models in aiding course design for standardized residency training: a case study on teaching the interpretation of chest X-ray plain film reports

Digital Intelligence MedicalEducation | 更新时间：2026-02-13

- Large language models in aiding course design for standardized residency training: a case study on teaching the interpretation of chest X-ray plain film reports
- Chinese Journal of Medical Education Research Vol. 25, Issue 1, Pages: 30-36(2026)
- 作者机构：
  
  1.上海交通大学医学院附属同仁医院影像科，上海 200336
  2.上海交通大学医学院附属同仁医院教学科，上海 200336
  3.上海交通大学医学院附属同仁医院院长办公室，上海 200336
  4.上海交通大学医学院附属瑞金医院《诊断学理论与实践》编辑部，上海 200025
  5.上海交通大学医学院附属瑞金医院放射科，上海 200025
- 作者简介：
  
  Yao Weiwu, Email: yaoweiwuhuan@163.com
- 基金信息：
  
  2025 Key Courses of Shanghai Higher Education [Medical Imaging (For Preventive Medicine)];2024 "Generative Artificial Intelligence + Education" Fund of Shanghai Jiao Tong University(CTLD24A0021);2024 Autumn "Artificial Intelligence + Education" Fund from Shanghai Jiao Tong University(CTLD24A0081);"Digital Intelligence Courses" Construction Project from Shanghai Jiao Tong University [Medical Imaging (For Preventive Medicine)];2024 Undergraduate Core Courses Construction Project from Shanghai Jiao Tong University School of Medicine [Medical Imaging (For Preventive Medicine)];2023 Undergraduate Textbook Construction Project from Shanghai Jiao Tong University School of Medicine [Medical Imaging (For Preventive Medicine)];Yingxing Project of Shanghai Tongren Hospital(TRYXJH28)
- DOI：10.3760/cma.j.cn116021-20240922-01963
  CLC： R-05
- Received：22 September 2024，
  
  Published：20 January 2026
- 稿件说明：
移动端阅览
钟京谕, 星月, 胡扬帆, 等. 大语言模型辅助住院医师规范化培训课程设计：以胸部X线平片报告教学为例[J]. 中华医学教育探索杂志, 2026, 25(1): 30-36.

Zhong Jingyu, Xing Yue, Hu Yangfan, et al. Large language models in aiding course design for standardized residency training: a case study on teaching the interpretation of chest X-ray plain film reports[J]. Chinese Journal of Medical Education Research, 2026, 25(1): 30-36.
钟京谕, 星月, 胡扬帆, 等. 大语言模型辅助住院医师规范化培训课程设计：以胸部X线平片报告教学为例[J]. 中华医学教育探索杂志, 2026, 25(1): 30-36. DOI： 10.3760/cma.j.cn116021-20240922-01963.

Zhong Jingyu, Xing Yue, Hu Yangfan, et al. Large language models in aiding course design for standardized residency training: a case study on teaching the interpretation of chest X-ray plain film reports[J]. Chinese Journal of Medical Education Research, 2026, 25(1): 30-36. DOI： 10.3760/cma.j.cn116021-20240922-01963.

摘要

目的

评估大语言模型（large language model，LLM）辅助住院医师规范化培训课程设计的能力和风险。

方法

采用个案研究的方法，以胸部X线平片报告教学为例模拟了医学课程设计过程。构建了简体中文提示词要求文心一言和ChatGPT生成课程设计内容，客观记录了两者的回复内容和用时，主观判定了其辅助课程设计的能力和风险。

结果

文心一言和ChatGPT单次回复字数的中位数（范围）分别为442（93~653）字和543（117~843）字，回复总字数分别为4 469字和5 362字，对话分别耗时13 min 38 s和12 min 25 s。两者均能辅助课程设计，具备快速编写教案、生成教学材料的能力，但创造性总结的能力较为欠缺，存在虚构内容的风险。

结论

大语言模型尚不具备独立设计住院医师规范化培训课程的能力，其生成的内容在用于教学实践前必须经过专家优化和确认后才能用于教学实践，但LLM依然有望成为医学课程设计的助手，甚至撬动医学教育的变革。

Abstract

Objective

To evaluate the ability and risk of using large language models to aid the course design for standardized residency training.

Methods

This study employed a case study approach to simulate the process of medical course design

using teaching the interpretation of chest X-ray plain film reports as an example. The prompt words were constructed in simplified Chinese for both ERNIE Bot and ChatGPT to conduct course design. Their responses and response times were objectively recorded

and their ability and risk in aiding course design were subjectively determined.

Results

The median (range) of the number of words replied by ERNIE Bot and ChatGPT in one session was 442 (93-653) and 543 (117-843)

the total number of words replied was 4 469 and 5 362

and the response time was 13 min 38 s and 12 min 25 s

respectively. Both large language models were able to aid the course design. They have the ability in quickly writing teaching plans and generating teaching materials. However

they showed limited capability for creative synthesis and carried a risk of generating fabricated information.

Conclusions

The large language models do not have the ability to independently design the courses for standardized residency training. The generated information should be optimized and confirmed by experts before being applied in teaching practice. However

large language models hold promise as assistants in medical course design and could potentially drive transformative changes in medical education.

关键词

Keywords

references

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