AIHealthcare Analytics

RAG Architecture for Clinical Use

Retrieval-Augmented Generation (RAG) extends standard LLM generation by grounding responses in verified external knowledge sources, reducing hallucinations and improving transparency. Med-KAG integrates a medical knowledge graph built from the UMLS Metathesaurus with LLM generation, grounding responses in verified biomedical relationships between diseases, symptoms, and treatments. Preliminary evaluation on MedQA-US shows comparable accuracy to a baseline LLM (Qwen3-235B-A22B), with the retriever identified as the primary source of error.

Hypergraph-Enhanced RAG

Hyper-RAG uses hypergraph-driven retrieval that captures both pairwise and beyond-pairwise correlations in domain-specific knowledge. In experiments on the NeurologyCrop dataset across six LLMs, Hyper-RAG improved accuracy by an average of 12.3% over direct LLM use and outperformed GraphRAG and LightRAG by 6.3% and 6.0% respectively. A lightweight variant, Hyper-RAG-Lite, achieved twice the retrieval speed with a 3.3% performance boost.

Ontology-Enhanced Retrieval

For complex rheumatological cases, ontology-based knowledge graphs combining SNOMED CT and clinical guidelines outperformed embedding-only retrieval. While embeddings suffice for simple cases, ontology-based knowledge graphs are crucial for complex reasoning tasks requiring structured medical relationship understanding.

Clinical Safety Guardrails

Safety guardrails in patient-facing LLM systems shape outcomes differently across task-risk levels. Provenance-related safeguards improved transparency more consistently than they ensured safe downstream action. Boundary-control strategies — source-bounded retrieval, clinician deferral, escalation support — became increasingly important as task actionability and interaction complexity increased. Most evidence concerns simulated tasks rather than sustained real-world deployment.