ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows

ProtoMedAgent introduces a framework that combines interpretable prototype networks with privacy-aware AI workflows to generate clinically accurate medical reports without the hallucination issues common in standard RAG systems. The approach achieves 91.2% faithfulness in clinical documentation while protecting patient privacy through k-anonymity and ℓ-diversity constraints.

ProtoMedAgent addresses a critical problem in clinical AI: the gap between visual diagnostic models and reliable medical documentation. Traditional RAG systems struggle with "retrieval sycophancy," where language models fabricate post-hoc explanations to align with visual predictions, creating unsafe conditions for patient care. This research formalizes clinical reporting as a constrained optimization problem, using a neuro-symbolic bottleneck that transforms frozen prototype features into discrete semantic memory. The Scribe-Critic loop enforces mathematical constraints preventing unsupported claims, fundamentally eliminating hallucination pathways.

The privacy dimension elevates this work beyond standard interpretability research. Healthcare systems face mounting pressure to deploy AI while maintaining HIPAA compliance and patient confidentiality. ProtoMedAgent's semantic privacy gate uses k-anonymity and ℓ-diversity to bound disclosure risk, reducing membership inference vulnerabilities by 9.8% through phase transitions in privacy parameters. This dual focus—accuracy and privacy—reflects industry demands for trustworthy AI in sensitive domains.

The 91.2% Comparison Set Faithfulness metric represents substantial improvement over RAG's 46.2%, indicating that constrained neuro-symbolic approaches outperform end-to-end language models in safety-critical applications. For healthcare AI developers, this demonstrates that strict architectural constraints need not sacrifice performance. The framework's test-time optimization without gradient updates suits deployment constraints in clinical settings where model retraining is impractical.

Future adoption depends on integration with existing electronic health record systems and regulatory acceptance of the faithfulness metrics. Broader implications extend to other regulated domains—finance, law, aviation—where hallucination-free AI reporting is mandatory.

• →ProtoMedAgent achieves 91.2% clinical documentation faithfulness versus 46.2% for standard RAG systems through neuro-symbolic constraints.
• →The framework mathematically precludes unsupported narrative claims using set-theoretic differentials and iterative Scribe-Critic validation loops.
• →Privacy-aware design reduces membership inference risks by 9.8% using k-anonymity and ℓ-diversity mechanisms.
• →Test-time optimization operates on frozen prototype backbones, enabling deployment without model retraining in clinical environments.
• →Approach demonstrates that architectural safety constraints can improve rather than compromise AI system performance in regulated domains.