AI and the World's Hard Problems · Medicine & drug discovery · Nature (computational biology) · 2026-10-26
Hybrid retrieval–verification framework for hallucination reduction in medical LLMs
Large language models used in clinical settings can produce answers that are not supported by evidence, which poses safety risks. The authors propose CARE-Med, a framework that combines dense retrieval, natural language inference verifiers and confidence scoring, and test it on MIMIC-III, PubMedQA and BioASQ. The NLI-based unsupported-response rate fell from 32% for a baseline LLM to 10%, and from 21% under conventional RAG to 10%.