用AI助手提升近视患者教育效果，支持图文问答。
ChatMyopia: An AI Agent for Pre-consultation Education in Primary Eye Care Settings
- 融合图像识别与医学知识库，实现图文问答。
- 随机对照试验显示满意度显著高于传统手册。
- 适合基层眼科医生用于个性化患者教育。
大型语言模型（LLMs）在个性化医疗沟通中展现潜力，但在可解释性和多任务整合方面面临挑战，尤其针对近视等专业领域，其在真实场景中的有效性尚未验证。本文提出ChatMyopia，一个基于LLM的AI助手，用于处理与近视相关的文本和图像查询。该系统集成图像分类工具及基于文献、专家共识和临床指南构建的检索增强知识库。通过近视黄斑病变分级任务、单题测试和人工评估，验证了其在提供个性化、准确且安全回答方面的表现，具备高可扩展性和可解释性。在一项随机对照试验中（n=70，NCT06607822），相比传统手册，ChatMyopia显著提升了患者满意度，在准确性、共情能力、疾病认知和医患沟通方面均有改善。结果表明，ChatMyopia有望作为重要补充，提升初级眼保健中的患者教育质量与服务满意度。
原文摘要 · Abstract (English)
Large language models (LLMs) show promise for tailored healthcare communication but face challenges in interpretability and multi-task integration particularly for domain-specific needs like myopia, and their real-world effectiveness as patient education tools has yet to be demonstrated. Here, we introduce ChatMyopia, an LLM-based AI agent designed to address text and image-based inquiries related to myopia. To achieve this, ChatMyopia integrates an image classification tool and a retrieval-augmented knowledge base built from literature, expert consensus, and clinical guidelines. Myopic maculopathy grading task, single question examination and human evaluations validated its ability to deliver personalized, accurate, and safe responses to myopia-related inquiries with high scalability and interpretability. In a randomized controlled trial (n=70, NCT06607822), ChatMyopia significantly improved patient satisfaction compared to traditional leaflets, enhancing patient education in accuracy, empathy, disease awareness, and patient-eyecare practitioner communication. These findings highlight ChatMyopia's potential as a valuable supplement to enhance patient education and improve satisfaction with medical services in primary eye care settings.
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