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

Natural Language Processing (937) Ambient AI Scribes and AI-Driven Documentation Technologies (20) Decision Support for Health Professionals (1594) Electronic Health Records (1354) Artificial Intelligence (2252) Clinical Informatics (1381)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76773, first published .
Toward Cross-Hospital Deployment of Natural Language Processing Systems: Model Development and Validation of Fine-Tuned Large Language Models for Disease Name Recognition in Japanese

Toward Cross-Hospital Deployment of Natural Language Processing Systems: Model Development and Validation of Fine-Tuned Large Language Models for Disease Name Recognition in Japanese

Toward Cross-Hospital Deployment of Natural Language Processing Systems: Model Development and Validation of Fine-Tuned Large Language Models for Disease Name Recognition in Japanese

Authors of this article:

Seiji Shimizu1 Author Orcid Image ;   Tomohiro Nishiyama1 Author Orcid Image ;   Hiroyuki Nagai1 Author Orcid Image ;   Shoko Wakamiya1 Author Orcid Image ;   Eiji Aramaki1 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Kim S, Lee D, Kim Y, Yoon H, Lee H. Beyond Fine-Tuning: Leveraging Domain-Aware In-Context learning with large language models for clinical named entity recognition. Journal of Biomedical Informatics 2026;174:104982 View