Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Thursday, July 24, 2025

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

AI is almost completely worthless until the underlying research for 100% recovery is there!  You're putting the cart before the horse!

Well I'd suggest dumping all stroke research into Dr. Watson of IBM  and see what comes out.

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


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

Authors of this article:

Shiqi Qiang1 Author Orcid Image ;  Haitao Zhang1, 2 Author Orcid Image ;  Yang Liao1, 3 Author Orcid Image ;  Yue Zhang1, 4 Author Orcid Image ;  Yanfen Gu1, 5 Author Orcid Image ;  Yiyan Wang1, 3 Author Orcid Image ;  Zehui Xu6 Author Orcid Image ;  Hui Shi7 Author Orcid Image ;  Nuo Han8 

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 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.

Methods:In 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 tests.

Results:The results revealed significant differences across the 4 models in 5 dimensions: accuracy (P=.002), completeness (P<.001), readability (P=.04), safety (P=.007), and humanity (P<.001). ChatGPT-4 outperformed all models in each dimension, with scores for accuracy (mean 4.28, SD 0.84), completeness (mean 4.35, SD 0.75), readability (mean 4.28, SD 0.85), safety (mean 4.38, SD0.81), and user-friendliness (mean 4.65, SD 0.66). MedGo excelled in accuracy (mean 4.06, SD 0.78) and completeness (mean 4.06, SD 0.74). Qwen and ERNIE Bot scored significantly lower across all 5 dimensions than ChatGPT-4 and MedGo. ChatGPT-4 generated the longest responses (mean 1338.35, SD 236.03) and had the highest readability score (mean 12.88). In the second phase, ChatGPT-4 performed the best overall, while MedGo provided the clearest responses.

Conclusions:LLMs, 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.

J Med Internet Res 2025;27:e73226

doi:10.2196/73226
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