arXiv 2503.041532025-03-06cs.AI

KidneyTalk-open：基于医学文档增强知识库的肾脏疾病私有大型语言模型无代码部署

KidneyTalk-open: No-code Deployment of a Private Large Language Model with Medical Documentation-Enhanced Knowledge Database for Kidney Disease

Tianjin University of Technology（天津理工大学）；National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Peking University First Hospital（北京大学第一医院）；Peking University People’s Hospital（北京大学人民医院）；Institute of Internet Industry, Tsinghua University（清华大学互联网产业研究院）；Chinese Academy of Medical Sciences（中国医学科学院）；State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University（北京大学血管稳态与重构国家重点实验室）

Yongchao Long, Chao Yang, Gongzheng Tang, Jinwei Wang, Zhun Sui, Yuxi Zhou, Shenda Hong, Luxia Zhang

备注：: Corresponding authors: zhanglx@bjmu.edu.cn; joy_yuxi@pku.edu.cn; hongshenda@pku.edu.cn

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AI总结：

针对肾脏疾病医疗决策的隐私保护与本地化部署难题，本文提出无代码桌面系统KidneyTalk-open，集成开源大模型无代码部署、医学文档处理流水线及智能体协作的自适应检索增强技术，显著提升了临床问答准确率与证据可追溯性。

摘要：

针对肾脏疾病的隐私保护医疗决策支持，需要在保持临床推理能力的同时实现大型语言模型（LLMs）的本地化部署。当前解决方案面临三大挑战：1）基于云端的LLMs存在数据安全风险；2）本地模型部署需要专业技术知识；3）通用LLMs缺乏整合医学知识的机制。检索增强系统在医学文档处理和临床可用性方面也存在困难。我们开发了KidneyTalk-open，这是一个集成了三项技术组件的桌面系统：1）通过本地推理引擎实现最先进（SOTA）开源LLMs（如DeepSeek-r1、Qwen2.5）的无代码部署；2）结合上下文感知分块和智能过滤的医学文档处理流水线；3）采用智能体协作的自适应检索与增强流水线（AddRep），以提高医学文档的召回率。设计的图形界面使临床医生无需技术专业知识即可管理医学文档并进行AI驱动的咨询。在1,455道具有挑战性的肾脏病学考试题上的实验验证证明了AddRep的有效性：通过智能知识整合实现了29.1%的准确率（比基线高8.1%），同时通过4.9%的弃权（不执行）率抑制幻觉来保持鲁棒性。与主流产品（AnythingLLM、Chatbox、GPT4ALL）的对比案例研究证明了KidneyTalk-open在实际临床查询中的优越性能。KidneyTalk-open是首个能在桌面上实现安全文档增强医疗问答的无代码医疗LLM系统。其设计为隐私敏感的临床AI应用建立了新框架。该系统显著降低了技术门槛，同时提高了证据可追溯性，使更多医务人员或患者能够便捷地使用SOTA开源LLMs。

英文摘要：

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.

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