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<p>Clinical trainees face limited opportunities to practice medical history-taking skills due to scarce case diversity and access to real patients. To address this, we developed a large language model-based digital patient (LLMDP) system that transforms de‑identified electronic health records into voice‑enabled virtual patients capable of free‑text dialog and adaptive feedback, based on our previously established open-source retrieval-augmented framework. In a single‑center randomized controlled trial (ClinicalTrials.gov: NCT06229379; <i>N</i> = 84), students trained with LLMDP achieved a 10.50-point increase in medical history-taking assessment scores (95% CI: 4.66–16.33, <i>p</i> &lt; 0.001) compared to those using traditional methods. LLMDP-trained students also demonstrated greater empathy. Participants reported high satisfaction with LLMDP, emphasizing its role in reducing training costs and boosting confidence for real patient interactions. These findings provide evidence that LLM-driven digital patients enhance medical history-taking skills and offer a scalable, low-risk pathway for integrating generative AI into ophthalmology education.</p>

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A large language model digital patient system enhances ophthalmology history taking skills

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月4日

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  • Ming-Jie Luo,
  • Shaowei Bi,
  • Jianyu Pang,
  • Lixue Liu,
  • Ching-Kit Tsui,
  • Yunxi Lai,
  • Wenben Chen,
  • Yahan Yang,
  • Kezheng Xu,
  • Lanqin Zhao,
  • Ling Jin,
  • Duoru Lin,
  • Xiaohang Wu,
  • Jingjing Chen,
  • Rongxin Chen,
  • Zhenzhen Liu,
  • Yuxian Zou,
  • Yangfan Yang,
  • Yiqing Li,
  • Haotian Lin

摘要

Clinical trainees face limited opportunities to practice medical history-taking skills due to scarce case diversity and access to real patients. To address this, we developed a large language model-based digital patient (LLMDP) system that transforms de‑identified electronic health records into voice‑enabled virtual patients capable of free‑text dialog and adaptive feedback, based on our previously established open-source retrieval-augmented framework. In a single‑center randomized controlled trial (ClinicalTrials.gov: NCT06229379; N = 84), students trained with LLMDP achieved a 10.50-point increase in medical history-taking assessment scores (95% CI: 4.66–16.33, p < 0.001) compared to those using traditional methods. LLMDP-trained students also demonstrated greater empathy. Participants reported high satisfaction with LLMDP, emphasizing its role in reducing training costs and boosting confidence for real patient interactions. These findings provide evidence that LLM-driven digital patients enhance medical history-taking skills and offer a scalable, low-risk pathway for integrating generative AI into ophthalmology education.