Performance of the large language model ChatGPT on the national nurse examinations in Japan: evaluation study
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Abstract
BACKGROUND: ChatGPT, a large language model, has shown good performance on physician certification examinations and medical consultations. However, its performance has not been examined in languages other than English or on nursing examinations. OBJECTIVE: We aimed to evaluate the performance of ChatGPT on the Japanese National Nurse Examinations. METHODS: We evaluated the percentages of correct answers provided by ChatGPT (GPT-3.5) for all questions on the Japanese National Nurse Examinations from 2019 to 2023, excluding inappropriate questions and those containing images. Inappropriate questions were pointed out by a third-party organization and announced by the government to be excluded from scoring. Specifically, these include "questions with inappropriate question difficulty" and "questions with errors in the questions or choices." These examinations consist of 240 questions each year, divided into basic knowledge questions that test the basic issues of particular importance to nurses and general questions that test a wide range of specialized knowledge. Furthermore, the questions had 2 types of formats: simple-choice and situation-setup questions. Simple-choice questions are primarily knowledge-based and multiple-choice, whereas situation-setup questions entail the candidate reading a patient's and family situation's description, and selecting the nurse's action or patient's response. Hence, the questions were standardized using 2 types of prompts before requesting answers from ChatGPT. Chi-square tests were conducted to compare the percentage of correct answers for each year's examination format and specialty area related to the question. In addition, a Cochran-Armitage trend test was performed with the percentage of correct answers from 2019 to 2023. RESULTS: The 5-year average percentage of correct answers for ChatGPT was 75.1% (SD 3%) for basic knowledge questions and 64.5% (SD 5%) for general questions. The highest percentage of correct answers on the 2019 examination was 80% for basic knowledge questions and 71.2% for general questions. ChatGPT met the passing criteria for the 2019 Japanese National Nurse Examination and was close to passing the 2020-2023 examinations, with only a few more correct answers required to pass. ChatGPT had a lower percentage of correct answers in some areas, such as pharmacology, social welfare, related law and regulations, endocrinology/metabolism, and dermatology, and a higher percentage of correct answers in the areas of nutrition, pathology, hematology, ophthalmology, otolaryngology, dentistry and dental surgery, and nursing integration and practice. CONCLUSIONS: ChatGPT only passed the 2019 Japanese National Nursing Examination during the most recent 5 years. Although it did not pass the examinations from other years, it performed very close to the passing level, even in those containing questions related to psychology, communication, and nursing.
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Some authors stated the use of prompt engineering, prompt learning, and direct instruction strategy via prompt to guide the use of the ChatGPT.
“Some authors stated the use of prompt engineering (Taira et al., 2023), prompt learning (Goktas et al., 2024), and direct instruction strategy via prompt (Zong, 2024) to guide the use of the ChatGPT.”
From Current trends and future implications in the utilization of ChatGPT in nursing: a rapid review (2024) · cites this paper as Taira et al., 2023
Other challenges include providing inaccurate or misleading information.
“providing inaccurate or misleading information (Taira et al., 2023)”
From Current trends and future implications in the utilization of ChatGPT in nursing: a rapid review (2024) · cites this paper as Taira et al., 2023
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- Authors
- Taira, K., Itaya, T., & Hanada, A.
- Affiliations
- Kyoto University (JP)
- Keio University (JP)
- Corresponding author
- Taira, Kazuya
- Venue
- JMIR Nursing
- Publisher
- JMIR Publications Inc.
- Published
- 2023-06-27
- Volume
- 6
- Pages
- e47305
- ISSN
- 2562-7600 (ISSN-L)
- PMC
- PMC10337249
- OpenAlex
- W4378528433
- Cited by
- 76
- References
- 12
- Citations by year
- 2026: 11 · 2025: 24 · 2024: 34 · 2023: 6 · 2022: 1
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Nursing (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Kyoto University Research Information Repository (Kyoto University) (submittedVersion, cc-by)
- Funders
- Japan Society for the Promotion of Science (22K21182, 22K17549)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- ChatGPT, large language models, nursing certification examination, multiple-choice questions, nursing knowledge assessment, social welfare, ChatGPT-3.5, ChatGPT-4
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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