SynthesisJournal of medical Internet research2024
Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis.
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.
What it found
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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.
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.
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Who cites it
138 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- The performance of ChatGPT and other large language models on multiple-choice questions in biomedical disciplines: A meta-analysis.Anatomical sciences education · 2026Pooled it
- Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis.International dental journal · 2026Pooled it
- The Effectiveness of Artificial Intelligence in Undergraduate Health Professions Education: Systematic Review and Meta-Analysis of Randomized Controlled Trials.JMIR medical education · 2026Pooled it
- Evaluating the Potential and Accuracy of ChatGPT-3.5 and 4.0 in Medical Licensing and In-Training Examinations: Systematic Review and Meta-Analysis.JMIR medical education · 2025Pooled it
- Evaluating the Performance of ChatGPT on Board-Style Examination Questions in Ophthalmology: A Meta-Analysis.Journal of medical systems · 2025Pooled it
- Large Language Models in Dental Licensing Examinations: Systematic Review and Meta-Analysis.International dental journal · 2025Pooled it
- Performance of large language models in medical licensing examinations: a systematic review and meta-analysis.Journal of educational evaluation for health professions · 2025Pooled it
- The performance of ChatGPT versus neurosurgery residents in neurosurgical board examination-like questions: a systematic review and meta-analysis.Neurosurgical review · 2024Pooled it
- Integrating Large Language Models Into Trauma Education for Medical Students: Randomized Controlled Pilot Trial.JMIR medical education · 2026Trial
- Exploring the Application Capability of ChatGPT as an Instructor in Skills Education for Dental Medical Students: Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Large Language Model Performance on Multistep Clinical Cases: Comparative Study Across Question and Case Levels.JMIR medical education · 2026Article
- Performance of Reasoning and Comparator Large Language Models on Nephrology Multiple-choice Questions.JMA journal · 2026Article
- Artificial intelligence in reproductive medicine and education: current evidence, challenges, and future directions.Journal of assisted reproduction and genetics · 2026Review
- AI Integration in Undergraduate Medical Education: Qualitative Study of Faculty Perspectives in the United Arab Emirates.JMIR medical education · 2026Article
- Evaluating Retrieval-Augmented Large Language Models on Anesthesiology Board-Style Questions: Benchmark Study.JMIR formative research · 2026Article
- Can ChatGPT pass the polish national medical specialization examination in orthopedics and traumatology?Archives of orthopaedic and trauma surgery · 2026Article
- 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 · 2026Article
- 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 · 2026Article
- Performance of multimodal large language models on image-based surgical anatomy, anatomical pathology, and radiology questions.Anatomical sciences education · 2026Article
- A 2-Tiered Rescue Protocol to Mitigate Difficulty-Based Failures of ChatGPT (GPT-5) and Gemini on the German M2 Medical Examination: Evaluation Study.JMIR formative research · 2026Article
78 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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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 Socratic graph.