ArticleJournal of medical education and curricular development
Performance of ChatGPT-4 on the Nepalese Undergraduate Medical Licensing Examination: A Cross-Sectional Study.
Article in Journal of medical education and curricular development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Performance of large language models on undergraduate endodontic multiple-choice questions.BMC oral health · 2026Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: ChatGPT has shown remarkable performance in medical licensing examinations such as the United States Medical Licensing Examination. However, limited research exists regarding its performance on national medical licensing exams in low-income countries. In Nepal, where nearly half of the candidates fail the national medical licensing exam, ChatGPT has the potential to contribute to medical education. Objective: To evaluate ChatGPT's (GPT-4) performance on the Nepal Medical Council Licensing Medical Examination (NMCLE). Methods: The NMCLE-May 2024 dataset, comprising 900 multiple-choice questions, was used to assess ChatGPT's performance. After excluding 8 questions that contained figures or were not compatible with text-only input, 892 questions were analyzed. Specific prompt, including a background description, question, and choices, was entered. The response generated by ChatGPT was compared taking responses from experienced clinicians as a reference. Descriptive statistics were used to present the results, and regression analysis was employed to determine the association between variables, including set, question type, pattern, and subject, and incorrect responses. Results: GPT-4 generated 783 correct responses in 892 questions, an accuracy rate of 87.8%. Incorrect responses were more likely with questions requiring logical reasoning (odds ratio 14.7, 95% confidence interval [CI] 8.94-24.16). Conclusions: ChatGPT-4 performs at a standard comparable to or above that of medical graduates on the Nepalese undergraduate medical licensing examination. Incorrect responses were mainly in questions requiring logical reasoning, underscoring the need for caution when relying on its outputs in the same. These findings are encouraging and highlight the need for further studies to evaluate its role as an educational resource in Nepalese medical education.
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Registered trials
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.