Privacy-Preserving Medical Knowledge Retrieval with Self-Supervised Hash Learning and Adversarial Defense for Healthcare AI Agents - Zhoukai Xue; Troy Fields; Ferry Hansson; Dean Wabb (Bioinformatics Insights and Analytics, 2026)
The rapid integration of artificial intelligence agents into clinical decision support systems has intensified the demand for medical knowledge retrieval pipelines that simultaneously ensure high-fidelity semantic access, rigorous privacy protection, and resilience against adversarial manipulation. This paper presents a system-level architectural investigation into privacy-preserving medical knowledge retrieval that unifies self-supervised hash learning with layered adversarial defense mechanisms. By mapping medical knowledge fragments into compact binary codes through self-supervised contrastive objectives, the framework enables efficient approximate nearest-neighbor search over distributed repositories without exposing sensitive clinical content. The proposed architecture couples a differentially private hash encoder with secure index structures and an adversarial sanitization module that monitors query integrity and defends against both input-space perturbations and knowledge-poisoning attacks. We examine structural trade-offs between hash code length, retrieval precision, computational latency, and achievable privacy guarantees, drawing on cross-domain insights from cryptographically secure computation, federated learning, and adversarial robustness literature. Deployment considerations are analyzed within the context of hospital information ecosystems and cross-border regulatory regimes, addressing governance challenges such as algorithmic auditing, fairness across heterogeneous patient populations, and the sustainability of large-scale hash-based retrieval infrastructure. The study further explores forward-looking policy implications for certifying autonomous healthcare AI agents that rely on privacy-preserving retrieval as a core cognitive operation. The analysis demonstrates that self-supervised hashing constitutes a promising foundation for trust-enhancing knowledge access, yet requires careful co-design with adversarial defense, governance frameworks, and lifecycle management to withstand emerging threat surfaces in medical decision-making environments.