[2505.24830] Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs

View the PDF file from the paper entitled Writing Improvement and the Clarification of Medical Questions that answer by verifying the atomic facts in LLMS that were armed, by Juraj Vladika and 12 other authors

PDF view

a summary:LLMS models show wide -ranging medical knowledge but vulnerable to hallucinations and inaccurate martyrdom, which is a challenge to its clinical adoption and organizational compliance. Current methods, such as the enhanced generation of retrieval, are in part these problems by grounding answers in source documents, but hallucinations and an explanation at the level of facts continues. In this work, we offer a new framework for atomic facts designed to enhance reliability and explain LLMS used to answer long medical questions. This method degrades the responses created by LLM to separate check -in units called atomic facts, each of which is independently verified against a reliable knowledge base of medical guidelines. This approach allows the targeted correction of errors and direct tracking to source literature, thus improving realistic accuracy and the ability to clarify medical questions and answers. Intensive assessment with multiple reader assessments by medical experts, the criterion of questions and answers automatically, has shown significant imprics and the ability to clarify. Our framework has achieved up to 40 % of the answer to the answer to the answer and the rate of hallucinogenic detection by 50 %. The ability to track each atomic truth to the most relevant pieces of the database provides a transparent and transparent explanation of the created responses, handling a major gap in the current artificial intelligence applications. This work represents a decisive step towards more worthy clinical applications and reliable LLMS, as it deals with the main requirements for clinical application and enhances greater confidence in artificial intelligence -backed health care.