The evolving landscape of large language models and non-large language models in health care

npj Health Systems 2026 Open access

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Authors: Rui Yang, Huitao Li, Matthew Yu Heng Wong, Yuhe Ke, Xin Li, Kunyu Yu, Jingchi Liao, Jonathan Chong Kai Liew, Sabarinath Vinod Nair, Jasmine Chiat Ling Ong, Irene Li, Douglas Teodoro
+4 more Chuan Hong, Yifan Peng, Daniel Shu Wei Ting, Nan Liu
DOI: 10.1038/s44401-026-00076-1
Cited by: 0
References: 30

Abstract

We analyzed 19,123 natural language processing-related studies to explore the differences in task distributions and application contexts between large language models (LLMs) and non-LLM methods in health care. Through topic modeling analysis, we found that LLMs demonstrate advantages in open-ended tasks, while non-LLM methods dominate in information extraction tasks. These findings highlight the complementary strengths of the two technical paradigms and provide reference for their integration strategies in future health care applications.

Fields of Study & Subjects

Topic Modeling Artificial Intelligence in Healthcare and Education Biomedical Text Mining and Ontologies

Publication details

Journal / source
npj Health Systems
Volume
3
Issue
1
Publication date
9 March 2026
Language
EN