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.
Figures & tables
Figure 1. Guideline-Structured RAG System Architecture. The system routes clinician queries to relevant guideline subsections, retrieves evidence in priority order (recommendations first, then structured criteria tables, then supporting text), generates guideline-aligned concise answers with explicit citations, validates that all claims are grounded in retrieved evidence, and presents results via a two-part interface: Concise Answer and Supporting Evidence . The structured guideline knowledge base preserves source attribution and enables full traceability.
Figure 2. ClinicBot’s interfaces demonstrate guideline-grounded clinical support. Left : Medical question answering interface, where clinicians submit patient queries and receive two-part responses: a concise answer (highlighted in green) followed by expandable supporting evidence with citations and clinical rationale. Right : Diabetes risk assessment interface, where clinicians input patient information and ClinicBot computes risk scores with clinical interpretation and actionable recommendations.