RAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering

Researchers propose RAG-X, a diagnostic framework for evaluating retrieval-augmented generation systems in medical AI applications. The study reveals an 'Accuracy Fallacy' showing a 14% gap between perceived system success and actual evidence-based grounding in medical question-answering systems.

• →RAG-X framework evaluates retriever and generator components independently across three types of medical QA tasks.
• →Current RAG evaluation benchmarks fail to diagnose whether errors stem from faulty retrieval or flawed generation.
• →The study identified an 'Accuracy Fallacy' with a 14% gap between perceived success and evidence-based grounding.
• →Context Utilization Efficiency (CUE) metrics help isolate verified grounding from deceptive accuracy in medical AI systems.
• →The framework addresses critical safety and verification needs for clinical RAG applications in healthcare AI.