Federated Deep Hashing and Trustworthy Large Language Model Agents for Secure Medical Imaging Decision Intelligence
Keywords:
federated learning, deep hashing, medical imaging, large language model agents, trustworthy AI, decision support, privacy preservationAbstract
The increasing volume and heterogeneity of medical imaging data demand intelligent decision support systems that can retrieve semantically relevant cases, reason over clinical evidence, and provide trustworthy recommendations while strictly preserving patient privacy. This paper proposes a novel integrative framework that couples federated deep hashing with trustworthy large language model (LLM) agents to enable secure, efficient, and interpretable medical imaging decision intelligence. Federated deep hashing allows distributed clinical sites to collaboratively learn compact binary codes for medical images without sharing raw data, leveraging self-supervised asymmetric semantic excavation and margin-scalable constraints to enhance retrieval precision and semantic coherence. In parallel, LLM agents are designed to consume retrieved neighbor cases and clinical metadata, generating contextualized diagnostic hypotheses, differential reasoning, and confidence-calibrated explanations under adversarial robustness and fairness constraints. The system-level architecture is examined through the lenses of structural trade-offs, privacy-utility equilibria, infrastructure heterogeneity, deployment sustainability, and multi-layered governance. We analyze the interplay between hashing granularity and agent reasoning fidelity, the challenges of maintaining model freshness across federated cohorts, and the policy implications of embedding generative agents into regulated clinical workflows. By synthesizing advances in distributed learning, semantic hashing, and agentic reasoning, the framework opens a pathway toward decentralized, auditable, and resilient medical AI ecosystems. The discussion extends to interoperability standards, carbon-aware training cycles, and continuous monitoring mechanisms that are essential for clinical translation. The paper provides a forward-looking perspective on how federated deep hashing and trustworthy LLM agents can jointly reshape secure medical imaging decision intelligence.
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