ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows

ArXi:2605.14113v1 Announce Type: cross While interpretable prototype networks offer compelling case-based reasoning for clinical diagnostics, their raw continuous outputs lack the semantic structure required for medical documentation. Bridging this gap via standard Retrieval-Augmented Generation (RAG) routinely triggers ``retrieval sycophancy,'' where Large Language Models (LLMs) hallucinate post-hoc rationalizations to align with visual predictions. We