Detecting Hallucinations in Healthcare AI

Author(s): Marie Humbert-Droz, PhD

Originally published on Towards AI.

Three Safety Layers Every Medical RAG System Needs

In our previous posts, we explored why hallucinations occur in healthcare LLMs and built a basic RAG system using medical literature from PubMed Central. While our RAG system reduced hallucinations by grounding answers in real medical evidence, we discovered a critical gap: even with citations, the system could still produce unreliable outputs.

This article discusses the critical need for reliable detection of hallucinations in healthcare AI systems, emphasizing three essential techniques: Source Attribution, Consistency Checking, and Semantic Entropy. Each technique addresses different risks associated with incorrect or unreliable outputs, providing a framework for achieving greater safety in medical AI applications. The article also explores the limitations of traditional retrieval methods and introduces a multi-stage system for handling complex queries, aiming for enhanced reliability in AI-generated medical information.

Read the full blog for free on Medium.

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Published via Towards AI