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This paper introduces a large language model-based digital patient (LLMDP) system that converts de-identified electronic health records into interactive, voice-enabled virtual patients for ophthalmology training. The LLMDP system, built upon a retrieval-augmented framework, allows for free-text dialogue and adaptive feedback. A randomized controlled trial (N=84) demonstrated that students trained with the LLMDP system significantly improved their medical history-taking assessment scores and empathy compared to those using traditional methods, suggesting a scalable and effective approach to medical education.
Forget rote memorization — LLMs can now simulate realistic patient interactions, boosting medical history-taking skills and empathy in ophthalmology trainees.
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.
Anthropic