Running summarizing tools on a new article

Abstract: Clinicians use Large Language Models (LLMs) for clinical decision support, medical question answering, and automated document writing. However, these models can hallucinate and pose safety risks. While Retrieval-Augmented Generation (RAG) improves factuality, it does not always produce outputs consistent with supporting evidence. To address these issues, this study reframes NLI-based unsupported-response rate mitigation for generative models as a confidence-aware inference problem and proposes a unified CARE-Med framework for retrieval, generation, and verification. The framework ensures the reliability of its outputs by leveraging dense retrieval, natural language inference verifiers, and a confidence scoring mechanism. The authors validated the anti-NLI-based unsupported-response optimisation method for biomedical large models proposed in this paper against the MIMIC-III, PubMedQA, and BioASQ benchmarks. The method reduces NLI-based unsupported-response rate decreases from 32% for the Baseline LLM to 10% for CARE-Med, corresponding to a 22-percentage-point or 68.8% relative reduction. Compared with conventional RAG, NLI-based unsupported-response rate decreases from 21% to 10%, corresponding to an 11-percentage-point or 52.4% relative reduction. The authors leverage explicit verification and uncertainty reasoning to meet requirements for safe clinical deployment.