ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910268571779072 |
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| author | Pellicer, Alvaro Lopez Angelov, Plamen Bukhari, Marwan Li, Yi Soares, Eduardo Kerns, Jemma |
| author_facet | Pellicer, Alvaro Lopez Angelov, Plamen Bukhari, Marwan Li, Yi Soares, Eduardo Kerns, Jemma |
| contents | 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 optimization problem over a strict neuro-symbolic bottleneck. Operating on a frozen prototype backbone, we distill latent visual and tabular features into a discrete semantic memory. Online generation is strictly constrained by exact set-theoretic differentials and a reflective Scribe-Critic loop, mathematically precluding unsupported narrative claims. To safely bound data disclosure, we introduce a semantic privacy gate governed by $k$-anonymity and $\ell$-diversity. Evaluated on a 4,160-patient clinical cohort, ProtoMedAgent achieves 91.2% Comparison Set Faithfulness where it fundamentally outperforms standard RAG (46.2%). ProtoMedAgent additionally leverages a binding $\ell$-diversity phase transition to systematically reduce artifact-level membership inference risks by an absolute 9.8%. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_14113 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows Pellicer, Alvaro Lopez Angelov, Plamen Bukhari, Marwan Li, Yi Soares, Eduardo Kerns, Jemma Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multiagent Systems 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 optimization problem over a strict neuro-symbolic bottleneck. Operating on a frozen prototype backbone, we distill latent visual and tabular features into a discrete semantic memory. Online generation is strictly constrained by exact set-theoretic differentials and a reflective Scribe-Critic loop, mathematically precluding unsupported narrative claims. To safely bound data disclosure, we introduce a semantic privacy gate governed by $k$-anonymity and $\ell$-diversity. Evaluated on a 4,160-patient clinical cohort, ProtoMedAgent achieves 91.2% Comparison Set Faithfulness where it fundamentally outperforms standard RAG (46.2%). ProtoMedAgent additionally leverages a binding $\ell$-diversity phase transition to systematically reduce artifact-level membership inference risks by an absolute 9.8%. |
| title | ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2605.14113 |