Multimodal Adversarial Defense Framework for Vision-Language Medical Agents in Intelligent Diagnostic Environments
Keywords:
multimodal adversarial defense, vision-language medical agents, diagnostic robustness, medical AI security, clinical decision support, trustworthy AI governance, intelligent healthcare infrastructureAbstract
The increasing deployment of vision-language models in clinical workflows has given rise to a new class of intelligent medical agents capable of jointly interpreting radiological images, electronic health records, and natural language queries. Despite their diagnostic promise, these multimodal systems inherit profound adversarial vulnerabilities that threaten patient safety, diagnostic equity, and institutional trust. This paper presents a comprehensive adversarial defense framework designed explicitly for vision-language medical agents operating in intelligent diagnostic environments. We reconceptualize robustness not as a post hoc patch but as a first-class architectural property spanning data ingestion, cross-modal alignment, reasoning transparency, and runtime governance. The framework integrates multi-layered defense strategies, including modality-specific sanitizers, cross-modal consistency verification, structured output constraints, and a policy enforcement layer grounded in regulatory standards. We examine structural trade-offs between detection latency and diagnostic throughput, explore fairness implications under adversarial perturbations that disproportionately affect underrepresented patient populations, and analyze sustainability concerns arising from continuous adversarial retraining. The discussion extends to deployment architectures across hospital edge servers and centralized cloud platforms, highlighting governance requirements for software as a medical device. Through cross-domain comparisons with autonomous vehicle perception pipelines and financial fraud detection systems, we distill lessons on fail-safe design and explainability. The proposed framework is not tied to a single model architecture but serves as a system-level blueprint for crafting resilient medical agents that maintain clinical accuracy and ethical integrity under evolving threat models. We conclude with a roadmap for regulatory co-design, continuous certification, and federated adversarial monitoring across healthcare institutions.
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