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

Artificial Intelligence (AI) in Medical Education (238) e-Learning and Digital Medical Education (975) New Methods and Approaches in Medical Education (429) mHealth in Medical Education and Training (140) Development and Evaluation of Research Methods, Instruments and Tools (829)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/81807, first published .
Large Language Models for the National Radiological Technologist Licensure Examination in Japan: Cross-Sectional Comparative Benchmarking and Evaluation of Model-Generated Items Study

Large Language Models for the National Radiological Technologist Licensure Examination in Japan: Cross-Sectional Comparative Benchmarking and Evaluation of Model-Generated Items Study

Large Language Models for the National Radiological Technologist Licensure Examination in Japan: Cross-Sectional Comparative Benchmarking and Evaluation of Model-Generated Items Study

Authors of this article:

Toshimune Ito1, 2, 3 Author Orcid Image ;   Toru Ishibashi1 Author Orcid Image ;   Tatsuya Hayashi1, 2 Author Orcid Image ;   Shinya Kojima1, 2, 4 Author Orcid Image ;   Kazumi Sogabe1, 5 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Sudo H, Noborimoto Y, Takahashi J. Evaluation of Few-Shot AI-Generated Feedback on Case Reports in Physical Therapy Education: Mixed Methods Study. JMIR Medical Education 2025;11:e85614 View
  2. Miki S, Hirano Y, Nakao T, Yamagishi Y, Hanaoka S, Nomura Y, Hamada A, Kanemaru N, Miyo R, Takahashi M, Hosoi R, Yoshikawa T, Abe O. AI achieves board-level performance on the Japan diagnostic radiology board examination through direct image interpretation. Japanese Journal of Radiology 2026 View