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

## TL;DR

This study compares large language models and traditional methods in healthcare, showing each excels in different tasks.

## Contribution

The paper identifies complementary strengths of LLMs and non-LLMs in healthcare through task distribution analysis.

## Key findings

- LLMs excel in open-ended tasks in healthcare applications.
- Non-LLM methods are more effective for information extraction tasks.
- The two approaches show complementary strengths in healthcare contexts.

## 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.