Application of Large Language Models in Stroke Rehabilitation Health Education: 2-Phase Study - Shiqi Qiang; Haitao Zhang; Yang Liao; Yue Zhang; Yanfen Gu; Yiyan Wang; Zehui Xu; Hui Shi; Nuo Han; Haiping Yu (Journal of Medical Internet Research)
Abstract BackgroundStroke 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. ObjectiveThis 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 aim is to offer patients with stroke more precise and secure health education pathways while exploring the feasibility of using LLMs to guide health education. MethodsIn the first phase of this study, a literature review and expert interviews identified 15 common questions and 2 clinical cases relevant to patients with stroke in home-based rehabilitation. These were input into 4 LLMs for simulated consultations. Six medical experts (2 clinicians, 2 nursing specialists, and 2 rehabilitation therapists) evaluated the LLM-generated responses using a Likert 5-point scale, assessing accuracy, completeness, readability, safety, and humanity. In the second phase, the top 2 performing models from phase 1 were selected. Thirty patients with stroke undergoing home-based rehabilitation were recruited. Each patient asked both models 3 questions, rated the responses using a satisfaction scale, and assessed readability, text length, and recommended reading age using a Chinese readability analysis tool. Data were analyzed using one-way ANOVA, post hoc Tukey Honestly Significant Difference tests, and paired t ResultsThe results revealed significant differences across the 4 models in 5 dimensions: accuracy (PPPPP ConclusionsLLMs, particularly ChatGPT-4 and MedGo, demonstrated promising performance in home-based stroke rehabilitation education. However, discrepancies between expert and patient evaluations highlight the need for improved alignment with patient comprehension and expectations. Enhancing clinical accuracy, readability, and oversight mechanisms will be essential for future real-world integration.