ArticleNPJ digital medicine2025
A large language model digital patient system enhances ophthalmology history taking skills.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06229379 (A Randomized Controlled Trial of the Effects of a Large Language Model on Medical Students' Clinical Questioning Skills), which is not on this map. Cited by 21 papers, 6 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Randomized Controlled Trial of the Effects of a Large Language Model on Medical Students' Clinical Questioning Skills
Who cites it
21 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- GenAI-Supported Virtual Patients in Health Care Education: Systematic Review.Journal of medical Internet research · 2026Pooled it
- The Effectiveness of Artificial Intelligence in Undergraduate Health Professions Education: Systematic Review and Meta-Analysis of Randomized Controlled Trials.JMIR medical education · 2026Pooled it
- The impact of integrating generative artificial intelligence into medical education on short-term learning outcomes: a systematic review and meta-analysis of randomized controlled trials.BMC medical education · 2026Pooled it
- Large Language Model-Based Virtual Patient Systems for History-Taking in Medical Education: Comprehensive Systematic Review.JMIR medical informatics · 2026Pooled it
- Virtual patients in medical education: a bibliometric analysis based on the wos core collection and scopus databases.Frontiers in medicine · 2026Pooled it
- Large language models for primary care ophthalmic education: a systematic review.Frontiers in medicine · 2026Pooled it
- A Patient Simulation Framework for Risk Assessment of Conversational Health Care AI: Development and Evaluation Study.JMIR AI · 2026Article
- Effect of Large Language Model-Powered Virtual Standardized Patients on History-Taking Among Undergraduate Medical Students: Propensity-Matched Cohort Study.JMIR medical education · 2026Article
- DFGLM-TCM: an integrated knowledge-and experience-driven large language model system for Traditional Chinese Medicine practice.Chinese medicine · 2026Article
- Comparative performance of chatgpt and gemini in diagnostic classification and clinical reasoning for open-angle glaucoma: a standardized scenario-based study.International ophthalmology · 2026Article
- IVCM-Insight: automated interactive interpretation of in vivo confocal microscopy.NPJ digital medicine · 2026Article
- AI-powered simulated patients with automated feedback for enhancing headache history-taking skills: a convergent mixed-methods study.BMC medical education · 2026Article
- Validated semi-supervised early and accurate screening for anterior segment diseases: a 3PM-guided conceptual and technological innovation.The EPMA journal · 2026Article
- Real-World Impact and Educational Effectiveness of an AI-Powered Medical History-Taking System: Retrospective Propensity Score-Matched Cohort Study.JMIR medical education · 2026Article
- Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.Frontiers in medicine · 2026Review
- AI-driven virtual standardized patients combined with scenario-based simulation in anesthesiology training: a randomized pilot study.Frontiers in medicine · 2026Article
- Evaluation of AI-generated versus registered dietitian-authored nutrition responses: a cross-sectional study.mHealth · 2026Article
- Virtual patient simulation to enhance medical students' clinical communication and decision-making skills: a pilot study.BMC medical education · 2025Article
- Review
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Corrections and comments
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Authors and funding
20 authors.
Funding
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 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.