ArticleJMIR medical education2023
Performance of ChatGPT on the Peruvian National Licensing Medical Examination: Cross-Sectional Study.
Article in JMIR medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 6 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 6 syntheses or guidelines pooled it, 88 citations in OpenAlex.
- 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
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 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 Accuracy and Capability of Artificial Intelligence Solutions in Health Care Examinations and Certificates: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Performance of ChatGPT-3.5 and GPT-4 in national licensing examinations for medicine, pharmacy, dentistry, and nursing: a systematic review and meta-analysis.BMC medical education · 2024Pooled it
- Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Performance Evaluation of Large Language Models in Multilingual Medical Multiple-Choice Questions: Mixed Methods Study.JMIR medical education · 2026Article
- Evaluating Large Language Models as Medical Consultation Tools for Double Eyelid Surgery: A Cross-Language Study in English and Chinese.Aesthetic plastic surgery · 2026Article
- Textbook-level medical knowledge in large language models: comparative evaluation using Japanese National Medical Examination.BMC medical informatics and decision making · 2026Article
- GPT-4o and OpenAI o1 Performance on the 2024 Spanish Competitive Medical Specialty Access Examination: Cross-Sectional Quantitative Evaluation Study.JMIR medical education · 2026Article
- What the performance of multimodal LLMs on a national licensing exam teaches us about occupational therapy education.BMC medical education · 2026Article
- Examining the Performance of ChatGPT in Comprehensive Pre-Internship Exam: The Potential of Artificial Intelligence in Medical Education.Health science reports · 2026Article
- A Multiagent Summarization and Auto-Evaluation Framework for Medical Text: Development and Evaluation Study.JMIR AI · 2025Article
- Evaluating the Readability and Quality of Bladder Cancer Information from AI Chatbots: A Comparative Study Between ChatGPT, Google Gemini, Grok, Claude and DeepSeek.Journal of clinical medicine · 2025Article
- GPT-4 shows comparable performance to human examiners in ranking open-text answers.Scientific reports · 2025Article
- Review
- Collaborative intelligence in AI: Evaluating the performance of a council of AIs on the USMLE.PLOS digital health · 2025Article
- Performance Evaluation of 18 Generative AI Models (ChatGPT, Gemini, Claude, and Perplexity) in 2024 Japanese Pharmacist Licensing Examination: Comparative Study.JMIR medical education · 2025Article
- Clinical Performance and Communication Skills of ChatGPT Versus Physicians in Emergency Medicine: Simulated Patient Study.JMIR medical informatics · 2025Article
- The role of artificial intelligence in medical education: an evaluation of Large Language Models (LLMs) on the Turkish Medical Specialty Training Entrance Exam.BMC medical education · 2025Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChatGPT has shown impressive performance in national medical licensing examinations, such as the United States Medical Licensing Examination (USMLE), even passing it with expert-level performance. However, there is a lack of research on its performance in low-income countries' national licensing medical examinations. In Peru, where almost one out of three examinees fails the national licensing medical examination, ChatGPT has the potential to enhance medical education.
objectiveWe aimed to assess the accuracy of ChatGPT using GPT-3.5 and GPT-4 on the Peruvian National Licensing Medical Examination (Examen Nacional de Medicina [ENAM]). Additionally, we sought to identify factors associated with incorrect answers provided by ChatGPT.
methodsWe used the ENAM 2022 data set, which consisted of 180 multiple-choice questions, to evaluate the performance of ChatGPT. Various prompts were used, and accuracy was evaluated. The performance of ChatGPT was compared to that of a sample of 1025 examinees. Factors such as question type, Peruvian-specific knowledge, discrimination, difficulty, quality of questions, and subject were analyzed to determine their influence on incorrect answers. Questions that received incorrect answers underwent a three-step process involving different prompts to explore the potential impact of adding roles and context on ChatGPT's accuracy.
resultsGPT-4 achieved an accuracy of 86% on the ENAM, followed by GPT-3.5 with 77%. The accuracy obtained by the 1025 examinees was 55%. There was a fair agreement (κ=0.38) between GPT-3.5 and GPT-4. Moderate-to-high-difficulty questions were associated with incorrect answers in the crude and adjusted model for GPT-3.5 (odds ratio [OR] 6.6, 95% CI 2.73-15.95) and GPT-4 (OR 33.23, 95% CI 4.3-257.12). After reinputting questions that received incorrect answers, GPT-3.5 went from 41 (100%) to 12 (29%) incorrect answers, and GPT-4 from 25 (100%) to 4 (16%).
conclusionsOur study found that ChatGPT (GPT-3.5 and GPT-4) can achieve expert-level performance on the ENAM, outperforming most of our examinees. We found fair agreement between both GPT-3.5 and GPT-4. Incorrect answers were associated with the difficulty of questions, which may resemble human performance. Furthermore, by reinputting questions that initially received incorrect answers with different prompts containing additional roles and context, ChatGPT achieved improved accuracy.
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