mlsanityData to 14 Oct 2026

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

arXiv 2503.04153 ↗First posted 6 Mar 2025cs.AIcited by 1

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 16 of its 28 references that are in the map. First posted on arXiv 6 Mar 2025.

Builds on

16 of 28 references.

References · 16

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  4. 2024Qwen2.5 Technical Reportcited by 4,942
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  6. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  7. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  8. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 861
  9. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  10. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  11. 2024Benchmarking Retrieval-Augmented Generation for Medicinecited by 331
  12. 2024MinerU: An Open-Source Solution for Precise Document Content Extractioncited by 267
4 more
  1. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 147
  2. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  3. 2024Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generationcited by 90
  4. 2024The Limited Impact of Medical Adaptation of Large Language and Vision-Language Modelscited by 9

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Preserving Privacy, Increasing Accessibility, and Reducing Cost: An On-Device Artificial Intelligence Model for Medical Transcription and Note Generationcited by 0

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  4. 2025A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases
  5. 2025Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework
  6. 2026ViSTA: A Simple Bridge Extends Visual Alignment to Clinical Time-Series Understanding in Multimodal LLMs
  7. 2024Retrieval Augmented Thought Process for Private Data Handling in Healthcare
  8. 2026MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization