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Objective <p>To evaluate the feasibility and educational value of employing large language models (LLMs) to generate clinical case scenario with multiple-choice questions (MCQs) for undergraduate medical education.</p> Methods <p>Twelve ophthalmology clinical case scenarios with MCQs generated by ChatGPT 4.0 were assessed for quality by eight teachers. High-scoring cases with MCQs were selected for review classes to test students’ learning. Student perceptions were collected via in-class and after-class questionnaires using a 5-point Likert scale.</p> Results <p>The average quality score of the 12 cases with MCQs was 52.33 ± 5.44 (range: 48–54.25; max = 60). There were statistical differences in the teachers’ scores for identical clinical cases (F = 16.050, <i>P</i> &lt; 0.001). Among 20 students, 95% agreed AI-generated cases enriched learning resources, 80% reported improved interdisciplinary integration and learning efficiency, while 85% used LLMs for post-class practice but raised concerns about content accuracy and difficulty calibration.</p> Conclusion <p>LLMs like ChatGPT can rapidly generate clinically relevant case scenarios and MCQs under precise prompts, offering a novel tool for educators and learners. However, expert review remains critical to mitigate risks of AI hallucinations (observed in 16.67% of cases, 2/12) and ensure alignment with curricular standards. Key issues included contradictions in imaging descriptions (e.g., inappropriate use of high-frequency ultrasound for chalazion) and diagnostic logic (e.g., inconsistent gonioscopy findings), underscoring the necessity of human oversight to refine content accuracy and educational utility.</p>

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Generative AI in medical education: feasibility and educational value of LLM-generated clinical cases with MCQs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月27日

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  • Qi Zhang,
  • Zijing Huang,
  • Yuqiang Huang,
  • Geng Wang,
  • Riping Zhang,
  • Jianling Yang,
  • Yinglin Cheng,
  • Binyao Chen,
  • Hongxi Wang,
  • Kunliang Qiu,
  • Haoyu Chen

摘要

Objective

To evaluate the feasibility and educational value of employing large language models (LLMs) to generate clinical case scenario with multiple-choice questions (MCQs) for undergraduate medical education.

Methods

Twelve ophthalmology clinical case scenarios with MCQs generated by ChatGPT 4.0 were assessed for quality by eight teachers. High-scoring cases with MCQs were selected for review classes to test students’ learning. Student perceptions were collected via in-class and after-class questionnaires using a 5-point Likert scale.

Results

The average quality score of the 12 cases with MCQs was 52.33 ± 5.44 (range: 48–54.25; max = 60). There were statistical differences in the teachers’ scores for identical clinical cases (F = 16.050, P < 0.001). Among 20 students, 95% agreed AI-generated cases enriched learning resources, 80% reported improved interdisciplinary integration and learning efficiency, while 85% used LLMs for post-class practice but raised concerns about content accuracy and difficulty calibration.

Conclusion

LLMs like ChatGPT can rapidly generate clinically relevant case scenarios and MCQs under precise prompts, offering a novel tool for educators and learners. However, expert review remains critical to mitigate risks of AI hallucinations (observed in 16.67% of cases, 2/12) and ensure alignment with curricular standards. Key issues included contradictions in imaging descriptions (e.g., inappropriate use of high-frequency ultrasound for chalazion) and diagnostic logic (e.g., inconsistent gonioscopy findings), underscoring the necessity of human oversight to refine content accuracy and educational utility.