ArticleDigital health
Performance evaluation of large language models for the national nursing examination in Japan.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Outcomes, Evaluation Metrics, and Measurement Tools for Large Language Model Applications Research in Nursing: A Scoping Review.International nursing review · 2026Article
- Comparative performance of artificial intelligence models in intensive care nursing questions: an evaluation of ChatGPT, DeepSeek, and Google Gemini.BMC nursing · 2026Article
- An Evaluation Framework for Large Language Models in Clinical Nursing: A Scoping Review and Expert Consultation.Journal of nursing management · 2026Article
- Rapid review on GenAI in nursing education.Frontiers in health services · 2025Review
- Assessment Design in the Era of Large Language Models: Evidence From Japanese Health Professions Licensing Examinations.Journal of medical education and curricular developmentArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Large language models (LLMs) are increasingly used in healthcare, with the potential for various applications. However, the performance of different LLMs on nursing license exams and their tendencies to make errors remain unclear. This study aimed to evaluate the accuracy of LLMs on basic nursing knowledge and identify trends in incorrect answers. Methods: The dataset consisted of 692 questions from the Japanese national nursing examinations over the past 3 years (2021-2023) that were structured with 240 multiple-choice questions per year and a total score of 300 points. The LLMs tested were ChatGPT-3.5, ChatGPT-4, and Microsoft Copilot. Questions were manually entered into each LLM, and their answers were collected. Accuracy rates were calculated to assess whether the LLMs could pass the exam, and deductive content analysis and Chi-squared tests were conducted to identify the tendency of incorrect answers. Results: For over 3 years, the mean total score and standard deviation (SD) using ChatGPT-3.5, ChatGPT-4, and Microsoft Copilot was 180.3 ± 22.2, 251.0 ± 13.1, and 256.7 ± 14.0, respectively. ChatGPT-4 and Microsoft Copilot showed sufficient accuracy rates to pass the examinations for all the years. All LLMs made more mistakes in the health support and social security system domains ( Conclusions: ChatGPT-4 and Microsoft Copilot may perform better than Chat GPT-3.5, and LLMs could incorrectly answer questions about laws and demographic data specific to a particular country.
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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.