Large Language Models for Health Care Text Classification: Systematic Review.

ABSTRACT:

Background

Large language models (LLMs) have fundamentally transformed approaches to natural language processing tasks across diverse domains. In health care, accurate and cost-efficient text classification is crucial-whether for clinical note analysis, diagnosis coding, or other related tasks-and LLMs present promising potential. Text classification has long faced multiple challenges, including the need for manual annotation during training, the handling of imbalanced data, and the development of scalable approaches. In health care, additional challenges arise, particularly the critical need to preserve patient data privacy and the complexity of medical terminology. Numerous studies have leveraged LLMs for automated health care text classification and compared their performance wi

SUBMITTER: Sakai H 

PROVIDER: S-EPMC12936667 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature