Evidence map›Paper›PMID 39052324›Full record

SynthesisJournal of medical Internet research2024

Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis.

Mingxin Liu, Tsuyoshi Okuhara, XinYi Chang, Ritsuko Shirabe, Yuriko Nishiie, Hiroko Okada, Takahiro Kiuchi

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
138citing papers in PubMed, 8 pooled it
–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

138 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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  17. Performance of nine large language models on real-world polysomnography interpretation for obstructive sleep apnea: a multi-dimensional comparative analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
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  18. Examining the Role of Artificial Intelligence in Assessment: A Comparative Study of ChatGPT and Educator-Generated Multiple-Choice Questions in a Dental Exam.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026
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78 more citing papers are in PubMed but not listed here.

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

7 authors.

Mingxin LiuDepartment of Health Communication, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID 0000-0001-6320-544X
Tsuyoshi OkuharaDepartment of Health Communication, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID 0000-0002-6251-3587
XinYi ChangDepartment of Industrial Engineering and Economics, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan.ORCID 0000-0002-8546-9494
Ritsuko ShirabeDepartment of Health Communication, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID 0000-0001-6427-210X
Yuriko NishiieDepartment of Health Communication, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID 0009-0005-9004-4182
Hiroko OkadaDepartment of Health Communication, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID 0000-0001-7877-9753
Takahiro KiuchiDepartment of Health Communication, School of Public Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID 0000-0001-5934-0681

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOver the past 2 years, researchers have used various medical licensing examinations to test whether ChatGPT (OpenAI) possesses accurate medical knowledge. The performance of each version of ChatGPT on the medical licensing examination in multiple environments showed remarkable differences. At this stage, there is still a lack of a comprehensive understanding of the variability in ChatGPT's performance on different medical licensing examinations.

objectiveIn this study, we reviewed all studies on ChatGPT performance in medical licensing examinations up to March 2024. This review aims to contribute to the evolving discourse on artificial intelligence (AI) in medical education by providing a comprehensive analysis of the performance of ChatGPT in various environments. The insights gained from this systematic review will guide educators, policymakers, and technical experts to effectively and judiciously use AI in medical education.

methodsWe searched the literature published between January 1, 2022, and March 29, 2024, by searching query strings in Web of Science, PubMed, and Scopus. Two authors screened the literature according to the inclusion and exclusion criteria, extracted data, and independently assessed the quality of the literature concerning Quality Assessment of Diagnostic Accuracy Studies-2. We conducted both qualitative and quantitative analyses.

resultsA total of 45 studies on the performance of different versions of ChatGPT in medical licensing examinations were included in this study. GPT-4 achieved an overall accuracy rate of 81% (95% CI 78-84; P<.01), significantly surpassing the 58% (95% CI 53-63; P<.01) accuracy rate of GPT-3.5. GPT-4 passed the medical examinations in 26 of 29 cases, outperforming the average scores of medical students in 13 of 17 cases. Translating the examination questions into English improved GPT-3.5's performance but did not affect GPT-4. GPT-3.5 showed no difference in performance between examinations from English-speaking and non-English-speaking countries (P=.72), but GPT-4 performed better on examinations from English-speaking countries significantly (P=.02). Any type of prompt could significantly improve GPT-3.5's (P=.03) and GPT-4's (P<.01) performance. GPT-3.5 performed better on short-text questions than on long-text questions. The difficulty of the questions affected the performance of GPT-3.5 and GPT-4. In image-based multiple-choice questions (MCQs), ChatGPT's accuracy rate ranges from 13.1% to 100%. ChatGPT performed significantly worse on open-ended questions than on MCQs.

conclusionsGPT-4 demonstrates considerable potential for future use in medical education. However, due to its insufficient accuracy, inconsistent performance, and the challenges posed by differing medical policies and knowledge across countries, GPT-4 is not yet suitable for use in medical education.

trial registrationPROSPERO CRD42024506687; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=506687.

Indexed as

Educational MeasurementLicensure, MedicalArtificial IntelligenceClinical CompetenceEducation, MedicalHumansartificial intelligencelanguage modelslarge language model, ChatGPT, medical licensing examination, medical educationLLMsmeta-analysisnatural language processingNLPreview methodssystematic

Identifiers

PMID39052324
PMCPMC11310649

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.