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

ABSTRACT: 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 r

SUBMITTER: Luo MJ 

PROVIDER: S-EPMC12322286 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature