mlsanityData to 14 Oct 2026

ClinicBot: A Guideline-Grounded Clinical Chatbot with Prioritized Evidence RAG and Verifiable Citations

arXiv 2605.00846 ↗First posted 11 Apr 2026cs.AI cs.MAcited by 2

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Abstract (arXiv)

Clinical diagnosis requires answers that are accurate, verifiable, and explicitly grounded in official guidelines. While large language models excel at natural language processing, their tendency to hallucinate undermines their utility in high-stakes medical contexts where precision is essential. Existing retrieval-augmented generation (RAG) systems treat all evidence equally, producing noisy context and generic answers misaligned with clinical practice. We present ClinicBot, an AI system that translates guideline recommendations into trustworthy clinical support through three key advances: (1) structured extraction of clinical guidelines into semantic units (recommendations, tables, definitions, narrative) with explicit provenance, (2) evidence prioritization that ranks content by clinical significance and guideline structure rather than textual similarity, and (3) a web-based interface that presents concise, actionable answers with verifiable evidence. We will demonstrate ClinicBot using diabetes questions from real patients and an additional diabetes risk assessment tool that is faithful to the American Diabetes Association (ADA) Standards of Care in Diabetes (2025). The demonstration will illustrate how semantic knowledge extraction and hierarchical evidence ranking can reliably operate in a multi-agent setting to process complex clinical guidelines at scale.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 11 Apr 2026.

Builds on

9 of 21 references.

References · 9

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  3. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  4. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  5. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  6. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  7. 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 185
  8. 2025MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilotcited by 83
  9. 2025A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcarecited by 27

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Rolescited by 2
  2. 2026From PDF to Evidence: Structure-Aware Retrieval for Clinical Practice Guidelinescited by 0

Similar, not cited

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  1. 2025Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines
  2. 2025Demo: Guide-RAG: Evidence-Driven Corpus Curation for Retrieval-Augmented Generation in Long COVID
  3. 2025Building a Human-Verified Clinical Reasoning Dataset via a Human LLM Hybrid Pipeline for Trustworthy Medical AI
  4. 2025Evaluating Large Language Models for Evidence-Based Clinical Question Answering
  5. 2026A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench
  6. 2026CPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Support
  7. 2025Advice for Diabetes Self-Management by ChatGPT Models: Challenges and Recommendations
  8. 2025MedGUIDE: Benchmarking Clinical Decision-Making in Large Language Models