Application of Large Language Models in Stroke Rehabilitation Health Education: 2-Phase Study.

ABSTRACT:

Background

Stroke is a leading cause of disability and death worldwide, with home-based rehabilitation playing a crucial role in improving patient prognosis and quality of life. Traditional health education often lacks precision, personalization, and accessibility. In contrast, large language models (LLMs) are gaining attention for their potential in medical health education, owing to their advanced natural language processing capabilities. However, the effectiveness of LLMs in home-based stroke rehabilitation remains uncertain.

Objective

This study evaluates the effectiveness of 4 LLMs-ChatGPT-4, MedGo, Qwen, and ERNIE Bot-selected for their diversity in model type, clinical relevance, and accessibility at the time of study design in home-based stroke rehabilitation. The ai

SUBMITTER: Qiang S 

PROVIDER: S-EPMC12306586 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature