Trial reportBMC medical education2026
Utilizing generative artificial intelligence to create simulated patient for history taking in gynecology.
Trial report in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThis study aimed to evaluate the effectiveness of a generative artificial intelligence based simulated patient model in improving gynecological history-taking skills among medical students, compared with traditional clinical teaching methods.
methodsA prospective randomized controlled trial was conducted involving 40 fourth-year medical students during their gynecology clerkship. Students were randomly assigned to either an AI group (n = 20) or a control group (n = 20). Both groups received identical theoretical instruction on history taking. The control group practiced interviews with instructor-led standardized patients, while the AI group conducted simulated interviews with a large language model configured to behave as a real patient using predefined gynecological cases. Outcomes were assessed using a gynecological history-taking content checklist, Objective Structured Clinical Examination (OSCE) Score, and the Mini-Clinical Evaluation Exercise (Mini-CEX). Statistical analyses were performed using t-tests, chi-square tests, or Mann-Whitney U tests.
resultsBaseline characteristics did not differ significantly between the two groups. Students in the AI group achieved a significantly higher history-taking checklist completion rate than those in the control group and missed fewer key history items. The AI group achieved significantly higher total OSCE scores compared to the control group. Mini-CEX results showed that the AI group performed significantly better in medical interviewing skills and organization/efficiency. No significant differences were observed in professionalism, clinical judgment, counseling skills, or overall clinical competence.
conclusionsThe use of a generative AI-simulated patient significantly enhanced the information completeness and organizational quality of gynecological history taking among medical students. AI-based simulation appears to be an effective and scalable adjunct to traditional standardized patients, offering educational benefits for reinforcing systematic clinical inquiry. However, these findings are preliminary as a single-center study with a limited sample size. Further large-scale, multi-center research is required to confirm the generalizability of these results across diverse educational settings.
Indexed as
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.