Evidence map›Paper›PMID 40674718›Full record

ArticleJMIR medical informatics2025

Clinical Performance and Communication Skills of ChatGPT Versus Physicians in Emergency Medicine: Simulated Patient Study.

ChulHyoung Park, Min Ho An, Gyubeom Hwang, Rae Woong Park, Juho An

Abstract readComparative Study
In one paragraph

Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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  10. Data Contamination in AI Evaluation.JMIR medical informatics · 2025
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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

5 authors.

ChulHyoung Park *Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.ORCID 0000-0003-0531-9144
Min Ho An *Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.ORCID 0000-0003-2773-9756
Gyubeom Hwang *Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.ORCID 0000-0002-2293-4555
Rae Woong ParkDepartment of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.ORCID 0000-0003-4989-3287
Juho AnDepartment of Emergency Medicine, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea, 82 0312195016.ORCID 0000-0002-7407-426X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Emergency medicine can benefit from artificial intelligence (AI) due to its unique challenges, such as high patient volume and the need for urgent interventions. However, it remains difficult to assess the applicability of AI systems to real-world emergency medicine practice, which requires not only medical knowledge but also adaptable problem-solving and effective communication skills. Objective: We aimed to evaluate ChatGPT's (OpenAI) performance in comparison to human doctors in simulated emergency medicine settings, using the framework of clinical performance examination and written examinations. Methods: In total, 12 human doctors were recruited to represent the medical professionals. Both ChatGPT and the human doctors were instructed to manage each case like real clinical settings with 12 simulated patients. After the clinical performance examination sessions, the conversation records were evaluated by an emergency medicine professor on history taking, clinical accuracy, and empathy on a 5-point Likert scale. Simulated patients completed a 5-point scale survey including overall comprehensibility, credibility, and concern reduction for each case. In addition, they evaluated whether the doctor they interacted with was similar to a human doctor. An additional evaluation was performed using vignette-based written examinations to assess diagnosis, investigation, and treatment planning. The mean scores from ChatGPT were then compared with those of the human doctors. Results: ChatGPT scored significantly higher than the physicians in both history-taking (mean score 3.91, SD 0.67 vs mean score 2.67, SD 0.78, P<.001) and empathy (mean score 4.50, SD 0.67 vs mean score 1.75, SD 0.62, P<.001). However, there was no significant difference in clinical accuracy. In the survey conducted with simulated patients, ChatGPT scored higher for concern reduction (mean score 4.33, SD 0.78 vs mean score 3.58, SD 0.90, P=.04). For comprehensibility and credibility, ChatGPT showed better performance, but the difference was not significant. In the similarity assessment score, no significant difference was observed (mean score 3.50, SD 1.78 vs mean score 3.25, SD 1.86, P=.71). Conclusions: ChatGPT's performance highlights its potential as a valuable adjunct in emergency medicine, demonstrating comparable proficiency in knowledge application, efficiency, and empathetic patient interaction. These results suggest that a collaborative health care model, integrating AI with human expertise, could enhance patient care and outcomes.

Indexed as

Artificial IntelligenceClinical CompetenceCommunicationEmergency MedicinePatient SimulationPhysiciansAdultFemaleGenerative Artificial IntelligenceHumansMalePhysician-Patient Relationsartificial intelligenceChatGPTclinical performance examinationclinical reasoningemergency medicineempathyhistory takinglarge language modelpatient experience

Identifiers

PMID40674718
PMCPMC12289221

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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.