“In healthcare, an AI hallucination isn’t just an error; it’s a liability. Standard RAG systems often fail when querying complex medical literature or Electronic Health Records (EHRs) due to context loss and retrieval drift. This session presents a Self-Correcting RAG Architecture designed for high-stakes clinical information retrieval.

We will deconstruct the ‘LLM Committee’ pattern: a multi-agent system where a Search Agent retrieves medical protocols, and an independent Grader Agent rigorously verifies the generated answer against the source text before presenting it to the clinician. We will demonstrate how to implement SQL RAG to accurately query structured patient vitals and Semantic Reranking to prioritize recent clinical guidelines. Attendees will leave with a blueprint for building AI assistants that provide verifiable, citation-backed answers for medical professionals.

Key Takeaways:

1. Zero-Hallucination Architectures: Implementing validation loops to ensure every AI claim is backed by a specific medical document.
2. Hybrid Data Retrieval: Strategies for querying unstructured medical journals and structured EHR data simultaneously.
3. Citation UX: Designing interfaces that force the AI to link directly to the source paragraph in the medical protocol.”