Evidence map›Paper›PMID 42480003›Full record

ArticleProceedings (Baylor University. Medical Center)2026

Enhancing objective structured clinical examination performance through an artificial intelligence virtual patient: a proof-of-concept study.

Betty S Chan, Jack Marjot, Timothy Dodds, Lucy S R Jo, Hyun Yang, Jen Xiang, Paul Dyer, Bhavani Kannan, Graham Hannah, Adrienne Torda

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings (Baylor University. Medical Center), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Deep thoughts about "deep" learning.Proceedings (Baylor University. Medical Center) · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Betty S ChanSchool of Clinical Medicine, University of New South Wales, Kensington, Australia.ORCID 0000-0003-0083-282X
Jack MarjotSchool of Clinical Medicine, University of New South Wales, Kensington, Australia.
Timothy DoddsImmersive Technologies, University of New South Wales, Kensington, Australia.
Lucy S R JoMedicine and Health Education Development Unit, University of New South Wales, Kensington, Australia.
Hyun YangSchool of Clinical Medicine, University of New South Wales, Kensington, Australia.
Jen XiangSchool of Clinical Medicine, University of New South Wales, Kensington, Australia.
Paul DyerImmersive Technologies, University of New South Wales, Kensington, Australia.
Bhavani KannanLearning and Teaching Group, UNSW Canberra, University of New South Wales, Kensington, Australia.
Graham HannahImmersive Technologies, University of New South Wales, Kensington, Australia.
Adrienne TordaSchool of Clinical Medicine, University of New South Wales, Kensington, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe application of artificial intelligence (AI) in simulating detailed patient-doctor interactions for objective structured clinical examinations (OSCEs) remains emerging. This study aimed to evaluate an AI virtual patient (AIVP) innovation designed to support medical education through interactive patient simulations and feedback.

methodThis prospective mixed-methods pilot recruited final-year medical students during their critical care term. Two cohorts were examined: a volunteer AIVP group (n = 43) and an educational-support medical education enhancement training (MEET) group (n = 8). Participants completed pre- and postintervention OSCEs scored across communication, history and examination, management, and overall outcome domains. Students accessed nine emergency medicine scenarios featuring AI-driven dialogue, speech recognition, avatar interaction, and immediate feedback. Quantitative outcomes were analyzed using the Wilcoxon signed-rank test. Postintervention surveys (Likert scale) and focus groups provided qualitative data, which were analyzed thematically.

resultsIn the AIVP cohort, descriptive improvements were observed across domains; however, Wilcoxon testing identified no statistically significant changes. MEET students demonstrated significant improvement in overall OSCE outcomes (median 37 [IQR: 30-67] vs 67 [IQR: 47-75];

conclusionAIVP was feasible and well received, providing practice aligned with university OSCE standards. While the volunteer cohort showed modest gains, the MEET group demonstrated significant improvement, suggesting benefit for students needing structured support. Tailored feedback and alignment with OSCE standards highlight AIVP as a promising adjunct for training.

Indexed as

Artificial IntelligenceClinical CompetenceEducational MeasurementPatient SimulationAdultFemaleHumansMalePilot ProjectsProof of Concept StudyProspective StudiesStudents, MedicalArtificial intelligencefeedbackobjective structured clinical examinationremediationvirtual patient

Identifiers

PMID42480003
PMCPMC13689783

What OpenQuestion holds

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Registered trials

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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.