用大模型推理提升医疗决策，减少误诊风险。
A Smart Multimodal Healthcare Copilot with Powerful LLM Reasoning
- 融合语音、病历等多模态输入，通过知识图谱增强推理
- 在公开和私有数据集上表现优于现有模型，诊断更准确
- 适合医生辅助诊断、医疗AI研究者参考
误诊给全球医疗系统带来严重危害，导致成本上升和患者风险增加。MedRAG 是一个具备强大大语言模型（LLM）推理能力的智能多模态医疗协作者，支持非侵入式语音监测、通用医疗查询及电子健康记录等多种输入方式，可提供诊断、治疗、用药及随访提问建议。其基于知识图谱引导的检索增强生成机制，能有效检索并整合关键诊断信息，降低误诊风险。在公共与私有数据集上的评估显示，MedRAG 在多项指标上超越现有模型，提供更具针对性和准确性的医疗辅助。演示视频见：https://www.youtube.com/watch?v=PNIBDMYRfDM，源代码地址：https://github.com/SNOWTEAM2023/MedRAG。
原文摘要 · Abstract (English)
Misdiagnosis causes significant harm to healthcare systems worldwide, leading to increased costs and patient risks. MedRAG is a smart multimodal healthcare copilot equipped with powerful large language model (LLM) reasoning, designed to enhance medical decision-making. It supports multiple input modalities, including non-intrusive voice monitoring, general medical queries, and electronic health records. MedRAG provides recommendations on diagnosis, treatment, medication, and follow-up questioning. Leveraging retrieval-augmented generation enhanced by knowledge graph-elicited reasoning, MedRAG retrieves and integrates critical diagnostic insights, reducing the risk of misdiagnosis. It has been evaluated on both public and private datasets, outperforming existing models and offering more specific and accurate healthcare assistance. A demonstration video of MedRAG is available at: https://www.youtube.com/watch?v=PNIBDMYRfDM. The source code is available at: https://github.com/SNOWTEAM2023/MedRAG.
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