无需编码即可部署私有医学大模型，实现肾病诊疗问答安全高效
KidneyTalk-open: No-code Deployment of a Private Large Language Model with Medical Documentation-Enhanced Knowledge Database for Kidney Disease
- 通过本地推理引擎实现无代码部署SOTA开源大模型
- 智能文档处理与自适应检索提升医学问答准确率29.1%（+8.1%）
- 适合临床医生、研究人员快速使用，保障数据隐私
针对肾病诊疗中医疗决策支持的隐私保护需求，现有方案存在三大挑战：云端大模型存在数据泄露风险；本地部署需专业技术；通用大模型缺乏医学知识融合机制。我们开发了KidneyTalk-open，一个桌面端系统，集成三项技术：1）通过本地推理引擎实现DeepSeek-r1、Qwen2.5等SOTA开源大模型的无代码部署；2）结合上下文感知分块与智能过滤的医学文档处理流程；3）采用代理协作的自适应检索增强管道（AddRep），提升医学文档召回率。图形化界面使临床医生可自主管理病历并开展AI咨询，无需技术背景。在1,455道复杂肾病考题上的实验表明，AddRep实现29.1%准确率（较基线提升8.1%），同时以4.9%拒答率有效抑制幻觉。与AnythingLLM、Chatbox、GPT4ALL的对比案例显示其在真实临床查询中表现更优。该系统是首个支持私有文档增强型医学问答的无代码桌面级医疗大模型平台，为隐私敏感的临床AI应用提供了新范式。
原文摘要 · Abstract (English)
Privacy-preserving medical decision support for kidney disease requires localized deployment of large language models (LLMs) while maintaining clinical reasoning capabilities. Current solutions face three challenges: 1) Cloud-based LLMs pose data security risks; 2) Local model deployment demands technical expertise; 3) General LLMs lack mechanisms to integrate medical knowledge. Retrieval-augmented systems also struggle with medical document processing and clinical usability. We developed KidneyTalk-open, a desktop system integrating three technical components: 1) No-code deployment of state-of-the-art (SOTA) open-source LLMs (such as DeepSeek-r1, Qwen2.5) via local inference engine; 2) Medical document processing pipeline combining context-aware chunking and intelligent filtering; 3) Adaptive Retrieval and Augmentation Pipeline (AddRep) employing agents collaboration for improving the recall rate of medical documents. A graphical interface was designed to enable clinicians to manage medical documents and conduct AI-powered consultations without technical expertise. Experimental validation on 1,455 challenging nephrology exam questions demonstrates AddRep's effectiveness: achieving 29.1% accuracy (+8.1% over baseline) with intelligent knowledge integration, while maintaining robustness through 4.9% rejection rate to suppress hallucinations. Comparative case studies with the mainstream products (AnythingLLM, Chatbox, GPT4ALL) demonstrate KidneyTalk-open's superior performance in real clinical query. KidneyTalk-open represents the first no-code medical LLM system enabling secure documentation-enhanced medical Q&A on desktop. Its designs establishes a new framework for privacy-sensitive clinical AI applications. The system significantly lowers technical barriers while improving evidence traceability, enabling more medical staff or patients to use SOTA open-source LLMs conveniently.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。