Luo Ming-Jie, Bi Shaowei, Pang Jianyu, Liu Lixue, Tsui Ching-Kit, Lai Yunxi, Chen Wenben, Yang Yahan, Xu Kezheng, Zhao Lanqin, Jin Ling, Lin Duoru, Wu Xiaohang, Chen Jingjing, Chen Rongxin, Liu Zhenzhen, Zou Yuxian, Yang Yangfan, Li Yiqing, Lin Haotian
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
NPJ Digit Med. 2025 Aug 4;8(1):502. doi: 10.1038/s41746-025-01841-6.
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.
由于病例多样性稀缺以及难以接触到真实患者，临床实习生练习病史采集技能的机会有限。为了解决这一问题，我们基于之前建立的开源检索增强框架，开发了一种基于大语言模型的数字患者（LLMDP）系统，该系统将去识别化的电子健康记录转化为能够进行自由文本对话和提供适应性反馈的语音虚拟患者。在一项单中心随机对照试验（ClinicalTrials.gov：NCT06229379；N = 84）中，与使用传统方法的学生相比，接受LLMDP培训的学生在病史采集评估分数上提高了10.50分（95%置信区间：4.66 - 16.33，p < 0.001）。接受LLMDP培训的学生也表现出更强的同理心。参与者对LLMDP表示高度满意，强调其在降低培训成本和增强与真实患者互动信心方面的作用。这些发现提供了证据，表明基于大语言模型驱动的数字患者可提高病史采集技能，并为将生成式人工智能整合到眼科教育中提供了一条可扩展、低风险的途径。
