John Snow Labs 51d ago

In healthcare, an AI hallucination is not a UX problem. It is a clinical liability. Standard RAG architectures fail when semantic similarity is mistaken for medical accuracy. Dippu Kumar Singh (Leader of Emerging Technologies at Fujitsu North America Inc.) presents “Trust but Verify: Architecting Self-Correcting RAG for Healthcare Decision Support” — a verification-first AI architecture designed for clinical environments where every generated claim must be traceable to source evidence. Timestamps: 00:00 Why hallucinations become clinical liability in healthcare AI 04:10 Why standard RAG breaks in clinical environments 08:20 Hybrid retrieval: vector search + SQL RAG for EHR systems 12:40 Citation verification, grader agents, and hallucination filtering 16:50 Building trustworthy clinical AI systems at production scale The core shift is architectural: moving from probabilistic generation to verification-first AI pipelines. Instead of relying on vector similarity alone, the system combines hybrid retrieval, SQL RAG, citation-enforced generation, semantic reranking, and an independent grader agent that audits every response before it reaches a clinician. The presentation breaks down why standard RAG systems fail in clinical settings: context loss from naive chunking, retrieval drift between similar medical concepts, and hallucinated outputs that become operational safety risks once connected to Electronic Health Records (EHRs). A major focus is the “LLM Committee” pattern: parallel generation, majority voting, deterministic hallucination filtering, and validation loops that reduce hallucination rates from 18% to 2.5% in complex healthcare queries. The session also explores the engineering trade-offs behind trustworthy clinical AI systems, including latency penalties, token cost, SQL schema integrity, Personally Identifiable Information (PII) protection, Small Language Models (SLMs), and multimodal verification pipelines for future healthcare deployment. 📌 Applied Healthcare AI Summit 2026 — what actually works in real-world healthcare AI, from pilots to production systems. #RAG #ClinicalAI #AIArchitecture #LLM #EHR #AISafety