This paper is in the following e-collection/theme issue:

Theme Issue: ChatGPT and Generative Language Models in Medical Education (101) Chatbots and Conversational Agents (626) New Methods and Approaches in Medical Education (310) e-Learning and Digital Medical Education (788) Clinical Information and Decision Making (1645) Development and Evaluation of Research Methods, Instruments and Tools (632) Machine Learning (1730) Artificial Intelligence (1720) Decision Support for Health Professionals (1337)

Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48452, first published .
The Potential of GPT-4 as a Support Tool for Pharmacists: Analytical Study Using the Japanese National Examination for Pharmacists

The Potential of GPT-4 as a Support Tool for Pharmacists: Analytical Study Using the Japanese National Examination for Pharmacists

The Potential of GPT-4 as a Support Tool for Pharmacists: Analytical Study Using the Japanese National Examination for Pharmacists

Authors of this article:

Yuki Kunitsu1 Author Orcid Image
Article Authors Cited by (22) Tweetations (4) Metrics

Journals

  1. Ohta K, Ohta S. The Performance of GPT-3.5, GPT-4, and Bard on the Japanese National Dentist Examination: A Comparison Study. Cureus 2023 View
  2. Bazzari F, Bazzari A. Utilizing ChatGPT in Telepharmacy. Cureus 2024 View
  3. Yamaguchi S, Morishita M, Fukuda H, Muraoka K, Nakamura T, Yoshioka I, Soh I, Ono K, Awano S. Evaluating the efficacy of leading large language models in the Japanese national dental hygienist examination: A comparative analysis of ChatGPT, Bard, and Bing Chat. Journal of Dental Sciences 2024;19(4):2262 View
  4. Noda M, Ueno T, Koshu R, Takaso Y, Shimada M, Saito C, Sugimoto H, Fushiki H, Ito M, Nomura A, Yoshizaki T. Performance of GPT-4V in Answering the Japanese Otolaryngology Board Certification Examination Questions: Evaluation Study. JMIR Medical Education 2024;10:e57054 View
  5. Sato H, Ogasawara K. ChatGPT (GPT-4) passed the Japanese National License Examination for Pharmacists in 2022, answering all items including those with diagrams: a descriptive study. Journal of Educational Evaluation for Health Professions 2024;21:4 View
  6. Berce C. Artificial intelligence generated clinical score sheets: looking at the two faces of Janus. Laboratory Animal Research 2024;40(1) View
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  8. Grossman S, Zerilli T, Nathan J. Appropriateness of ChatGPT as a resource for medication‐related questions. British Journal of Clinical Pharmacology 2024 View
  9. Ishida K, Hanada E. Potential of ChatGPT to Pass the Japanese Medical and Healthcare Professional National Licenses: A Literature Review. Cureus 2024 View
  10. Ishida K, Arisaka N, Fujii K. Analysis of Responses of GPT-4 V to the Japanese National Clinical Engineer Licensing Examination. Journal of Medical Systems 2024;48(1) View
  11. Yu H, Fan L, Li L, Zhou J, Ma Z, Xian L, Hua W, He S, Jin M, Zhang Y, Gandhi A, Ma X. Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis. Journal of Healthcare Informatics Research 2024;8(4):658 View
  12. Jin H, Lee H, Kim E. 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 2024;24(1) View
  13. Tokgöz Kaplan T, Cankar M. Evidence‐Based Potential of Generative Artificial Intelligence Large Language Models on Dental Avulsion: ChatGPT Versus Gemini. Dental Traumatology 2025;41(2):178 View
  14. Taniguchi M, Lindsey J. Performance of chatbots in queries concerning fundamental concepts in photochemistry. Photochemistry and Photobiology 2024 View
  15. Morishita M, Fukuda H, Yamaguchi S, Muraoka K, Nakamura T, Hayashi M, Yoshioka I, Ono K, Awano S. An exploratory assessment of GPT-4o and GPT-4 performance on the Japanese National Dental Examination. The Saudi Dental Journal 2024;36(12):1577 View
  16. Ishida K, Hanada E. ChatGPT (GPT-4V) Performance on the Healthcare Information Technologist Examination in Japan. Cureus 2025 View
  17. Yang H, Hu M, Most A, Hawkins W, Murray B, Smith S, Li S, Sikora A. Evaluating accuracy and reproducibility of large language model performance on critical care assessments in pharmacy education. Frontiers in Artificial Intelligence 2025;7 View
  18. Yagahara A, Uesugi M, Yokoi H. Exploration of the optimal deep learning model for english-Japanese machine translation of medical device adverse event terminology. BMC Medical Informatics and Decision Making 2025;25(1) View
  19. Zhang J, Wang J, Zhang J, Xia X, Zhou Z, Zhou X, Wu Y. Young Adult Perspectives on Artificial Intelligence–Based Medication Counseling in China: Discrete Choice Experiment. Journal of Medical Internet Research 2025;27:e67744 View
  20. Alghitran A, AlOsaimi H, Albuluwi A, Almalki E, Aldowayan A, Alharthi R, Qattan J, Alghamdi F, AlHalabi M, Almalki N, Alharthi A, Alshammari A, Kanan M. Integrating ChatGPT as a Tool in Pharmacy Practice: A Cross-Sectional Exploration Among Pharmacists in Saudi Arabia. Integrated Pharmacy Research and Practice 2025;Volume 14:31 View
  21. Maruyama J, Maki S, Furuya T, Nagashima Y, Kitagawa K, Toki Y, Iwata S, Yazaki M, Kitamura T, Gushiken S, Noguchi Y, Miura M, Inoue M, Shiga Y, Inage K, Orita S, Ohtori S. Retrieval-augmented generation enhances large language model performance on the Japanese orthopedic board examination. Journal of Orthopaedic Science 2025 View
  22. Kiyomiya K, Aomori T, Kawazoe H, Ohtani H. Current Use of Generative Artificial Intelligence in Pharmacy Practice: A Literature Mini-review. Iryo Yakugaku (Japanese Journal of Pharmaceutical Health Care and Sciences) 2025;51(4):177 View