Evidence map›Paper›PMID 39979969›Full record

ArticleBMC medical education2025

AI-powered standardised patients: evaluating ChatGPT-4o's impact on clinical case management in intern physicians.

Selcen Öncü, Fulya Torun, Hilal Hatice Ülkü

Abstract read
In one paragraph

Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 pooled it
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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

24 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

3 authors.

Selcen ÖncüDepartment of Medical Education, Faculty of Medicine, Aydın Adnan Menderes University, Aydın, Turkey. selcenoncu@hotmail.com.ORCID https://orcid.org/0000-0001-6329-4227
Fulya TorunDepartment of Computer and Instructional Technologies, Faculty of Education, Aydın Adnan Menderes University, Aydın, Turkey.ORCID https://orcid.org/0000-0001-6942-888X
Hilal Hatice ÜlküAydın Vocational School, Aydın Adnan Menderes University, Aydın, Turkey.ORCID https://orcid.org/0000-0003-1780-3531

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial Intelligence is currently being applied in healthcare for diagnosis, decision-making and education. ChatGPT-4o, with its advanced language and problem-solving capabilities, offers an innovative alternative as a virtual standardised patient in clinical training. Intern physicians are expected to develop clinical case management skills such as problem-solving, clinical reasoning and crisis management. In this study, ChatGPT-4o's served as virtual standardised patient and medical interns as physicians on clinical case management. This study aimed to evaluate intern physicians' competencies in clinical case management; problem-solving, clinical reasoning, crisis management and explore the impact and potential of ChatGPT-4o as a viable tool for assessing these competencies.

methodsThis study used a simultaneous triangulation design, integrating quantitative and qualitative data. Conducted at Aydın Adnan Menderes University, with 21 sixth-year medical students, ChatGPT-4o simulated realistic patient interactions requiring competencies in clinical case management; problem-solving, clinical reasoning, crisis management. Data were gathered through self-assessment survey, semi-structured interviews, observations of the students and ChatGPT-4o during the process. Analyses included Pearson correlation, Chi-square, and Kruskal-Wallis tests, with content analysis conducted on qualitative data using MAXQDA software for coding.

resultsAccording to the findings, observation and self-assessment survey scores of intern physicians' clinical case management skills were positively correlated. There was a significant gap between participants' self-assessment and actual performance, indicating discrepancies in self-perceived versus real clinical competence. Participants reported feeling inadequate in their problem-solving and clinical reasoning competencies and experienced time pressure. They were satisfied with the Artificial Intelligence-powered standardised patient process and were willing to continue similar practices. Participants engaged with a uniform patient experience. Although participants were satisfied, the application process was sometimes negatively affected due to disconnection problems and language processing challenges.

conclusionsChatGPT-4o successfully simulated patient interactions, providing a controlled environment without risking harm to real patients for practicing clinical case management. Although some of the technological challenges limited effectiveness, it was useful, cost-effective and accessible. It is thought that intern physicians will be better supported in acquiring clinical management skills through varied clinical scenarios using this method. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Artificial IntelligenceCase ManagementClinical CompetenceInternship and ResidencyPatient SimulationAdultClinical ReasoningFemaleGenerative Artificial IntelligenceHumansMaleProblem SolvingArtificial intelligenceChatGPT-4oClinical case managementIntern physiciansMedical educationStandardised patients

Identifiers

PMID39979969
PMCPMC11843762

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.