Search

The rapid evolution of artificial intelligence (AI) in healthcare demands innovative architectures that prioritize real-time decision-making in dynamic clinical settings. This conceptual manuscript introduces the edge-deployed hospital adaptive response topology (EHART), a novel intelligence loop designed for seamless integration into hospital ecosystems. EHART leverages edge computing to process multimodal clinical data locally, minimizing latency while ensuring interoperability with electronic health records (EHRs) and decision support systems. By orchestrating a closed-loop feedback mechanism, the framework addresses governance challenges, including AI drift monitoring, ethical data exchange, and resource-efficient analytics. Theoretical analysis highlights how EHART enhances clinical workflow resilience through adaptive intelligence cycles, reducing monitoring burdens and propagating decision confidence across interconnected nodes. Key components include layered data ingestion, real-time inference engines, and governance overlays that align with interoperability standards. Without relying on empirical evaluations, this work synthesizes recent literature on clinical AI infrastructures to propose a scalable model for smart hospitals. Implications include fostering trustworthy AI deployments in resource-constrained environments, emphasizing theoretical frameworks for risk assessment and system dynamics. Ultimately, EHART represents a paradigm for future-proofing hospital intelligence in real-time clinical contexts, balancing innovation with regulatory compliance.