Trial reportBMC medical education2025
Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trial.
Trial report in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06766383 (Feasibility Study of Using GPT for History-taking Training in Medical Education), which is not on this map. Cited by 17 papers, 4 of them syntheses that pooled it.
What it found
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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.
Feasibility Study of Using GPT for History-taking Training in Medical Education
Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The impact of generative artificial intelligence on clinical skills and knowledge acquisition in medical undergraduates: a systematic review and meta-analysis.BMC medical education · 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
- Comparison of artificial intelligence assisted training and traditional learning paths in clinical simulation skills training: meta-analysis of randomized controlled trials.Frontiers in medicine · 2026Pooled it
- The effectiveness of GAI-assisted teaching methods in medical education: a systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- Artificially Intelligent Simulated Patients in Undergraduate Surgical Teaching: A Single Blinded Randomized Controlled Study.The clinical teacher · 2026Trial
- Effectiveness of ChatGPT and DeepSeek in Urology Medical Education: Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- A Medical Large Language Model-Based System Improves History-Taking Performance Among Medical Students.Cureus · 2026Article
- Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review.Journal of medical Internet research · 2026Article
- Training Empathetic Communication Skills in Medical Students With a Role-Prompted GPT-4o Chatbot: Quasi-Experimental Intervention Study.JMIR medical education · 2026Article
- Feasibility of Large Language Model-Based Standardized Virtual Patients to Support Clinical Decision-Making Training in Operative Dentistry: Mixed Methods Study.JMIR formative research · 2026Article
- Use of Digital Technology for Developing Communication Skills in Undergraduate and Postgraduate Medical Education: Scoping Review.JMIR medical education · 2026Article
- Safety-First Framework for AI-Enabled Anamnesis in Head and Neck Surgery: Evidence Synthesis from a Narrative Review.Journal of clinical medicine · 2026Review
- Real-World Impact and Educational Effectiveness of an AI-Powered Medical History-Taking System: Retrospective Propensity Score-Matched Cohort Study.JMIR medical education · 2026Article
- Artificial intelligence in undergraduate medical education clinical skills curricula: a scoping review of implementations since 2022.Frontiers in digital health · 2026Review
- Effects of Artificial Intelligence-Supported Education on Critical Thinking, Problem-Solving, Clinical Reasoning, and Decision-Making in Health Science Education: A Systematic Review of Interventional Studies.Advances in medical education and practice · 2026Review
- Comparing Artificial intelligence to physicians' competences in the domain of clinical reasoning: A systematic review and meta-analysis.Journal of medical education and curricular developmentReview
- Learners are talking: Are teachers listening?Canadian family physician Medecin de famille canadienArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundsTraditional methods of teaching history-taking in medical education are limited by scalability and resource intensity. This study aims to assess the effectiveness of simulated patient interactions based on a custom-designed Generative Pre-trained Transformer (GPT) model, developed using OpenAI's ChatGPT GPTs platform, in enhancing medical students' history-taking skills compared to traditional role-playing methods.
methodsA total of 56 medical students were randomly assigned into two groups: an GPT group using GPT-simulated patients and a control group using traditional role-playing. Pre- and post-training assessments were conducted using a structured clinical examination to measure students' abilities in history collection, clinical reasoning, communication skills, and professional behavior. Additionally, students' evaluations of the educational effectiveness, satisfaction, and recommendation likelihood were assessed.
resultsThe GPT-simulation group showed significantly higher post-training scores in the structured clinical examination compared to the control group (86.79 ± 5.46,73.64 ± 4.76, respectively, P < 0.001). Students in the GPT group exhibited higher enthusiasm for learning, greater self-directed learning motivation, and better communication feedback abilities compared to the control group (P < 0.05). Additionally, the student satisfaction survey revealed that the GPT group rated higher on the diversity of diseases encountered, ease of use, and likelihood of recommending the training compared to the control group (P < 0.05).
conclusionsGPT-based history-taking training effectively enhances medical students' history-taking skills, providing a solid foundation for the application of artificial intelligence (AI) in medical education. CLINICAL TRIAL NUMBER: NCT06766383.
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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.