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

Hajar Sakai¹, Sarah S Lam¹

¹Binghamton University, Binghamton, NY, United States.

|February 11, 2026

Summary

Large language models (LLMs) significantly improve health care text classification accuracy over traditional methods. This review synthesizes evidence on LLM applications, highlighting their superior performance in tasks like clinical decision support.

Keywords:

health care large language models natural language processing neural networks systematic review text classification

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Area of Science:

Artificial Intelligence in Medicine
Natural Language Processing
Health Informatics

Background:

Large language models (LLMs) offer advanced capabilities for natural language processing tasks.
Accurate and efficient text classification is vital in healthcare for applications like clinical note analysis and diagnosis coding.
Existing systematic reviews lack a specific focus on LLMs for healthcare text classification.

Purpose of the Study:

To systematically review and critically evaluate the literature on LLM applications for text classification in healthcare.
To synthesize current evidence regarding the performance and methodologies of LLMs in healthcare text classification.

Main Methods:

A systematic literature search was conducted across major databases (PubMed, Scopus, etc.) for papers published between 2018 and 2024.
Studies were categorized by classification type, application, methodology, text type, and evaluation metrics, adhering to PRISMA guidelines.
65 eligible research articles were included in the review.

Main Results:

A significant increase in publications from 2020 to Q3 2024, with 28 papers in the first three quarters of 2024.
Fine-tuning (35 papers) and prompt engineering (17 papers) were the most common LLM approaches.
LLMs consistently outperformed traditional machine learning methods in healthcare text classification, particularly in clinical decision support applications.

Conclusions:

LLMs demonstrate superior performance in healthcare text classification compared to traditional machine learning.
Further research is needed to address identified gaps in the literature and explore future directions.
The review provides a comprehensive overview of LLM utilization in healthcare text classification.