Retrieval-Augmented Generation for Medical Question Answering on a Heart Failure Dataset: Performance Analysis

The implementation of a structured RAG framework paired with LLM classifiers for medical QA introduces a promising avenue for enhancing clinical decision support systems. By systematically analyzing the impact of query taxonomy, retrieval configurations, and response strategies, this approach clarifies the relative importance of each component within the medical RAG system using a HF dataset. Our findings provide actionable guidance on optimal design choices for maximizing retrieval and response accuracy; thus, informing the development of robust, scalable medical QA systems.