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

AI Language Models in Health Care (742) Natural Language Processing (1277) Big Data (435) Methods and Instruments in Medical Informatics (415) Precision Medicine and Genetics (104) Secondary Use of Clinical Data for Research and Surveillance (634) Machine Learning (3167) AI applications in genomics and pathology informatics, including generative AI (44) Generative Language Models Including ChatGPT (1514)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65047, first published .
Test tubes with pink liquid in front of a DNA helix on a screen

Evaluating and Enhancing Japanese Large Language Models for Genetic Counseling Support: Comparative Study of Domain Adaptation and the Development of an Expert-Evaluated Dataset

Evaluating and Enhancing Japanese Large Language Models for Genetic Counseling Support: Comparative Study of Domain Adaptation and the Development of an Expert-Evaluated Dataset

Authors of this article:

Takuya Fukushima1 Author Orcid Image ;   Masae Manabe2 Author Orcid Image ;   Shuntaro Yada1, 3 Author Orcid Image ;   Shoko Wakamiya1 Author Orcid Image ;   Akiko Yoshida4 Author Orcid Image ;   Yusaku Urakawa5, 6, 7 Author Orcid Image ;   Akiko Maeda5 Author Orcid Image ;   Shigeyuki Kan8 Author Orcid Image ;   Masayo Takahashi8 Author Orcid Image ;   Eiji Aramaki1 Author Orcid Image
Article Authors Cited by (9) Tweetations (3) Metrics

Journals

  1. Kerkez M, Kaplan M. Evaluation of discharge training given by nurses to postpartum mothers to artificial intelligence: an alternative approach to health care. BMC Nursing 2025;24(1) View
  2. Amugongo L, Mascheroni P, Brooks S, Doering S, Seidel J, Liu X. Retrieval augmented generation for large language models in healthcare: A systematic review. PLOS Digital Health 2025;4(6):e0000877 View
  3. Wang Y, Wan Y, Lei X, Chen Q, Hu H. A retrieval augmented generation based optimization approach for medical knowledge understanding and reasoning in large language models. Array 2025;28:100504 View
  4. Abumelha M, AL-Ghamdi A, Fayoumi A, Ragab M. Medical Feature Extraction From Clinical Examination Notes: Development and Evaluation of a Two-Phase Large Language Model Framework. JMIR Medical Informatics 2025;13:e78432 View
  5. Changalidis A, Barbitoff Y, Nasykhova Y, Glotov A. A systematic review on the generative AI applications in human medical genetics. Frontiers in Genetics 2026;16 View
  6. Tamayo L, Nishiyama T, Peng S, Wakamiya S, Aramaki E. A Multiassessment and Multiprofessional Agents Approach for Medical Chatbot Risk Estimation: Development and Evaluation Study. JMIR Medical Informatics 2026;14:e80416 View
  7. Powis Z, Liaquat K, Tschirgi M. AI’m Here to Help: Enhancing Laboratory Genetic Counseling with Artificial Intelligence. The Journal of Applied Laboratory Medicine 2026 View
  8. Haga S, MacNeal M. The impact of the rise of the chatbots: transforming genetics clinical delivery and research. Personalized Medicine 2026;23(3):235 View
  9. Zhong W, Huang J, Wei M, Liang Q, Yang Y, Chen J, Mao J, Qin Y, Sun Y, Liang Y. Performance and limitations of four large language models in genetic counseling for thalassemia. Frontiers in Digital Health 2026;8 View