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

2026-05-05 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Medical Devices & Health Technology, Clinical Care & Medical Practice · Depth: Advanced, long

Summary

ClinicBot is an AI system designed to provide accurate, verifiable, and guideline-grounded clinical support, addressing the hallucination issues common in large language models (LLMs) within high-stakes medical contexts. Developed on March 13, 2026, it enhances traditional retrieval-augmented generation (RAG) by structuring clinical guidelines into semantic units (recommendations, tables, narrative) with explicit provenance. The system prioritizes evidence based on clinical significance and guideline structure, rather than mere textual similarity, and presents concise, actionable answers with verifiable citations via a web-based interface. ClinicBot was demonstrated using diabetes questions from real patients and a diabetes risk assessment tool faithful to the American Diabetes Association (ADA) Standards of Care in Diabetes (2025), achieving 96% combined accuracy across 30 curated questions.

Key takeaway

For NLP Engineers developing clinical decision support systems, ClinicBot's approach highlights the necessity of moving beyond generic RAG. You should prioritize semantic knowledge extraction and hierarchical evidence ranking to ensure answers are accurate, verifiable, and aligned with clinical authority, thereby mitigating hallucination risks and enhancing trust in AI-driven medical applications.

Key insights

ClinicBot provides trustworthy clinical support by prioritizing guideline evidence and preventing LLM hallucinations.

Principles

• Clinical significance dictates evidence ranking.
• Traceability from answer to source is critical.
• Structured knowledge extraction improves RAG.

Method

ClinicBot constructs a structured knowledge base from guidelines, routes queries to relevant sections, retrieves evidence in a prioritized order (recommendations > tables > narrative), generates LLM-based answers, and validates claims against retrieved evidence.

In practice

• Use GPT-4o with low temperatures (T=0.1) for routing and generation.
• Extract guideline content into JSON with source attribution.
• Implement LLM-based validation for claim grounding and numeric matching.

Topics

• ClinicBot
• Prioritized Evidence RAG
• Clinical Decision Support
• Hallucination Prevention
• Diabetes Guidelines

Code references

• run-llama/llama_index

Best for: NLP Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.