Evaluating the Effectiveness and User Experience of Retrieval-Augmented Large Language Models for Supporting Stroke Patients and Their Caregivers

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Stroke is a leading cause of death and disability globally, with particularly high rates in China among older populations.
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Large language models (LLMs) like ChatGPT show potential in healthcare but struggle with accuracy and reliability in medical contexts.
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Retrieval-augmented generation (RAG) combines information retrieval with LLMs, improving accuracy and source validation for healthcare applications.
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This study aims to evaluate a RAG question-answering system for stroke-related queries, comparing three leading LLMs under different configurations.
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The research integrates AI technology with clinical needs, assessing LLM performance through technical metrics and end-user usability.

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