KidneyTalk-open: No-code Deployment of a Private Large Language Model with Medical Documentation-Enhanced Knowledge Database for Kidney Disease
Journal:
arXiv
Published Date:
Mar 6, 2025
Abstract
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.
