Improving Reliability and Explainability of Medical Question Answering Through Atomic Fact-Checking in Retrieval-Augmented Large Language Models: Creation and Validation Study
Abstract
Background: Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and show low fact-level explainability, limiting clinical adoption and regulatory compliance. Existing approaches, such as retrieval-augmented generation, partially address these issues by grounding answers in source documents; however, the aforementioned problems persist.
Objective: We propose the application of an atomic fact-checking framework designed to enhance the reliability and explainability of LLMs in medical long-form question answering. By decomposing generated answers into discrete atomic facts and verifying each against an authoritative knowledge base of medical guidelines, this approach enables precise identification and correction of incorrect statements, alongside explicit linkage to supporting literature.
Methods: The fact-checking algorithm operates within a retrieval-augmented generation framework: LLM-generated answers are decomposed into atomic facts (smallest and self-contained information units), each of which is assessed and corrected if FALSE. To determine an optimal strategy, the validation-question and answer (Q&A) set on prostate cancer treatment was tested under varying instructions. An extensive evaluation, including multireader assessments by human medical experts and the automated open Q&A benchmark AMEGA (Autonomous Medical Evaluation for Guideline Adherence), was conducted for the final pipeline. In addition to another radiooncologic…
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