Evidence record 4362 · automatically gathered

ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows

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 reportin

Record details

Published: 13 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Privacy · Healthcare · Agents & autonomy
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (13 May 2026), “ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows,” evidence record 4362, https://ethics.ai/record/4362 (originally published by arXiv).

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