ArticleJMIR formative research2023
Accuracy of ChatGPT on Medical Questions in the National Medical Licensing Examination in Japan: Evaluation Study.
Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 5 of them syntheses that pooled 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.
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.
Who cites it
46 citing papers in PubMed, 5 syntheses or guidelines pooled 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
- 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
- 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 of artificial intelligence chatbots in providing feeding management and oral health guidance for children with cleft lip and palate: ChatGPT 5.2 vs Gemini 3 Pro.European journal of pediatrics · 2026Article
- Generative artificial intelligence-driven chatbots and medical misinformation: an accuracy, referencing and readability audit.BMJ open · 2026Article
- Evaluation of large Language models on pediatric asthma: a comparative study of Claude3-Opus, Gemini 2.0, ChatGPT-4o, and DeepSeek-a cross-sectional questionnaire study.BMC medical informatics and decision making · 2026Article
- What the performance of multimodal LLMs on a national licensing exam teaches us about occupational therapy education.BMC medical education · 2026Article
- Undergraduate nursing students' attitudes and needs regarding the use of generative artificial intelligence in professional learning: a qualitative study.Frontiers in public health · 2026Article
- Quality Assessment of Large Language Model-Generated Medical Dialogue for Clinical Vignettes: Evaluation Study.JMIR formative research · 2025Article
- Review
- Performance Evaluation of 18 Generative AI Models (ChatGPT, Gemini, Claude, and Perplexity) in 2024 Japanese Pharmacist Licensing Examination: Comparative Study.JMIR medical education · 2025Article
- Evaluating large language model performance to support the diagnosis and management of patients with primary immune disorders.The Journal of allergy and clinical immunology · 2025Article
- Developing artificial intelligence tools for institutional review board pre-review: A pilot study on ChatGPT's accuracy and reproducibility.PLOS digital health · 2025Article
- Comparative Analysis of the Response Accuracies of Large Language Models in the Korean National Dental Hygienist Examination Across Korean and English Questions.International journal of dental hygiene · 2025Article
- Evaluation of the Performance of Three Large Language Models in Clinical Decision Support: A Comparative Study Based on Actual Cases.Journal of medical systems · 2025Article
- Exploration of the optimal deep learning model for english-Japanese machine translation of medical device adverse event terminology.BMC medical informatics and decision making · 2025Article
- ChatGPT and Other Large Language Models in Medical Education - Scoping Literature Review.Medical science educator · 2025Review
- Evaluating and Enhancing Japanese Large Language Models for Genetic Counseling Support: Comparative Study of Domain Adaptation and the Development of an Expert-Evaluated Dataset.JMIR medical informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChatGPT (OpenAI) has gained considerable attention because of its natural and intuitive responses. ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers, as stated by OpenAI as a limitation. However, considering that ChatGPT is an interactive AI that has been trained to reduce the output of unethical sentences, the reliability of the training data is high and the usefulness of the output content is promising. Fortunately, in March 2023, a new version of ChatGPT, GPT-4, was released, which, according to internal evaluations, was expected to increase the likelihood of producing factual responses by 40% compared with its predecessor, GPT-3.5. The usefulness of this version of ChatGPT in English is widely appreciated. It is also increasingly being evaluated as a system for obtaining medical information in languages other than English. Although it does not reach a passing score on the national medical examination in Chinese, its accuracy is expected to gradually improve. Evaluation of ChatGPT with Japanese input is limited, although there have been reports on the accuracy of ChatGPT's answers to clinical questions regarding the Japanese Society of Hypertension guidelines and on the performance of the National Nursing Examination.
objectiveThe objective of this study is to evaluate whether ChatGPT can provide accurate diagnoses and medical knowledge for Japanese input.
methodsQuestions from the National Medical Licensing Examination (NMLE) in Japan, administered by the Japanese Ministry of Health, Labour and Welfare in 2022, were used. All 400 questions were included. Exclusion criteria were figures and tables that ChatGPT could not recognize; only text questions were extracted. We instructed GPT-3.5 and GPT-4 to input the Japanese questions as they were and to output the correct answers for each question. The output of ChatGPT was verified by 2 general practice physicians. In case of discrepancies, they were checked by another physician to make a final decision. The overall performance was evaluated by calculating the percentage of correct answers output by GPT-3.5 and GPT-4.
resultsOf the 400 questions, 292 were analyzed. Questions containing charts, which are not supported by ChatGPT, were excluded. The correct response rate for GPT-4 was 81.5% (237/292), which was significantly higher than the rate for GPT-3.5, 42.8% (125/292). Moreover, GPT-4 surpassed the passing standard (>72%) for the NMLE, indicating its potential as a diagnostic and therapeutic decision aid for physicians.
conclusionsGPT-4 reached the passing standard for the NMLE in Japan, entered in Japanese, although it is limited to written questions. As the accelerated progress in the past few months has shown, the performance of the AI will improve as the large language model continues to learn more, and it may well become a decision support system for medical professionals by providing more accurate information.
Indexed as
Identifiers
What OpenQuestion holds
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.