arXiv:2605.14113v2 Announce Type: replace-cross Abstract: 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 introduce ProtoMedAgent, a framework that formalizes multimodal clinical reporting as an iterative, zero-gradient test-time
The proliferation of advanced AI models in sensitive fields like medicine necessitates novel solutions for interpretability and hallucination control, where current RAG approaches are proving insufficient.
This development is crucial for integrating AI into high-stakes clinical decision-making, addressing critical issues of trust, safety, and regulatory compliance through verifiable explanations and reduced model 'sycophancy'.
The paradigm for multimodal clinical AI interpretability shifts from raw continuous outputs and basic RAG to structured, privacy-aware agentic workflows, improving semantic accuracy and preventing post-hoc rationalizations.
- · AI-powered diagnostic companies
- · Healthcare providers
- · Medical AI researchers
- · Patients
- · Developers of uninterpretable 'black box' AI models
- · Medical AI solutions reliant solely on basic RAG
- · Companies with poor data privacy practices
Improved accuracy and trustworthiness of AI in medical diagnostics and reporting.
Accelerated adoption of AI in clinical settings due to enhanced interpretability and reduced risk of errors.
New regulatory frameworks and standards specifically addressing interpretability and hallucination in medical AI, potentially leading to 'interpretable AI' as a mandated feature.
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Read at arXiv cs.LG