Evidence map›Paper›PMID 40393017›Full record

ArticleJMIR medical education2025

Assessing ChatGPT's Capability as a New Age Standardized Patient: Qualitative Study.

Joseph Cross, Tarron Kayalackakom, Raymond E Robinson, Andrea Vaughans, Roopa Sebastian, Ricardo Hood, Courtney Lewis, Sumanth Devaraju, Prasanna Honnavar, Sheetal Naik and 8 more

Abstract read
In one paragraph

Article in JMIR medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

18 authors.

Joseph Cross *Medical University of the Americas, PO Box 701, Charlestown, Saint Kitts and Nevis, 1 9788629500 ext 364.ORCID 0000-0002-4680-2186
Tarron Kayalackakom *Department of Education Enhancement, College of Medicine, American University of Antigua, St Johns, Antigua and Barbuda.ORCID 0000-0002-4062-2539
Raymond E RobinsonDepartment of Health Informatics, School of Professional Studies, Northwestern University, Evanston, IL, United States.ORCID 0009-0000-8600-216X
Andrea VaughansDepartment of Biochemistry, Cell Biology and Genetics, College of Medicine, American University of Antigua, Basseterre, Antigua and Barbuda.ORCID 0009-0003-6694-0222
Roopa SebastianDepartment of Biochemistry, Cell Biology and Genetics, College of Medicine, American University of Antigua, Basseterre, Antigua and Barbuda.ORCID 0000-0003-2733-4812
Ricardo HoodDepartment of Education Enhancement, College of Medicine, American University of Antigua, St Johns, Antigua and Barbuda.ORCID 0000-0002-6321-1310
Courtney LewisDepartment of Education Enhancement, College of Medicine, American University of Antigua, St Johns, Antigua and Barbuda.ORCID 0009-0007-3458-1762
Sumanth DevarajuDepartment of Education Enhancement, College of Medicine, American University of Antigua, St Johns, Antigua and Barbuda.ORCID 0009-0006-3201-9963
Prasanna HonnavarDepartment of Education Enhancement, College of Medicine, American University of Antigua, St Johns, Antigua and Barbuda.ORCID 0000-0003-3872-4242
Sheetal NaikDepartment of Education Enhancement, College of Medicine, American University of Antigua, St Johns, Antigua and Barbuda.ORCID 0000-0001-5198-7421
Jillwin JosephDepartment of Education Enhancement, College of Medicine, American University of Antigua, St Johns, Antigua and Barbuda.ORCID 0000-0002-2592-6761
Nikhilesh AnandDepartment of Medical Education, School of Medicine, University of Texas Rio Grande Valley, Edinburgh, TX, United States.ORCID 0000-0002-2986-3550
Abdalla MohammedSchool of Medicine, Xavier University, Orangestad, Aruba.ORCID 0000-0003-1482-3692
Asjah JohnsonSchool of Medicine, Xavier University, Orangestad, Aruba.ORCID 0009-0005-8812-7325
Eliran CohenSchool of Medicine, Xavier University, Orangestad, Aruba.ORCID 0009-0004-4709-3459
Teniola AdenijiSchool of Medicine, Xavier University, Orangestad, Aruba.ORCID 0009-0003-4363-7431
Aisling Nnenna NnajiSchool of Medicine, Xavier University, Orangestad, Aruba.ORCID 0009-0007-8747-3642
Julia Elizabeth GeorgeSchool of Medicine, Xavier University, Orangestad, Aruba.ORCID 0009-0005-4921-1380

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Standardized patients (SPs) have been crucial in medical education, offering realistic patient interactions to students. Despite their benefits, SP training is resource-intensive and access can be limited. Advances in artificial intelligence (AI), particularly with large language models such as ChatGPT, present new opportunities for virtual SPs, potentially addressing these limitations. objectives: This study aims to assess medical students' perceptions and experiences of using ChatGPT as an SP and to evaluate ChatGPT's effectiveness in performing as a virtual SP in a medical school setting. Methods: This qualitative study, approved by the American University of Antigua Institutional Review Board, involved 9 students (5 females and 4 males, aged 22-48 years) from the American University of Antigua College of Medicine. Students were observed during a live role-play, interacting with ChatGPT as an SP using a predetermined prompt. A structured 15-question survey was administered before and after the interaction. Thematic analysis was conducted on the transcribed and coded responses, with inductive category formation. Results: Thematic analysis identified key themes preinteraction including technology limitations (eg, prompt engineering difficulties), learning efficacy (eg, potential for personalized learning and reduced interview stress), verisimilitude (eg, absence of visual cues), and trust (eg, concerns about AI accuracy). Postinteraction, students noted improvements in prompt engineering, some alignment issues (eg, limited responses on sensitive topics), maintained learning efficacy (eg, convenience and repetition), and continued verisimilitude challenges (eg, lack of empathy and nonverbal cues). No significant trust issues were reported postinteraction. Despite some limitations, students found ChatGPT as a valuable supplement to traditional SPs, enhancing practice flexibility and diagnostic skills. Conclusions: ChatGPT can effectively augment traditional SPs in medical education, offering accessible, flexible practice opportunities. However, it cannot fully replace human SPs due to limitations in verisimilitude and prompt engineering challenges. Integrating prompt engineering into medical curricula and continuous advancements in AI are recommended to enhance the use of virtual SPs.

Indexed as

Patient SimulationStudents, MedicalAdultArtificial IntelligenceEducation, Medical, UndergraduateFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedQualitative ResearchSurveys and QuestionnairesYoung AdultAIassessmentChatGPTdiagnosticeffectivenessflexibilityLLMmedical educationmedical schoolqualitativestandardized patientstandardized patientsvirtual patient

Identifiers

PMID40393017
PMCPMC12111480

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.