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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.

This systematic review examines the use of edge artificial intelligence (AI) and wearable sensors for real-time patient monitoring in smart hospitals and home settings, focusing on detecting deterioration, falls, arrhythmias, and infection-related changes. The review synthesizes studies from 2017 to 2026 on edge AI architectures, wearable sensor fusion, and clinical alert systems, emphasizing latency, power constraints, alert performance, and integration into clinical workflows. A PRISMA 2020-compliant search identified 127 studies from 2,100 records, with findings showing that while edge AI execution grew post-2020, it still represented a minority of designs. Sensor fusion was often linked to broader event coverage but increased implementation complexity. The review concludes that edge AI can reduce latency and enhance privacy but introduces challenges related to power usage, model complexity, device reliability, and maintenance, with limited clinical validation of alert systems and few studies addressing alert fatigue or clinician response.

Nurse call buttons are a core communication channel through which hospitalised patients request assistance, reassurance, symptom support, and routine care. When call demand is high, nurses may experience overload, interruptions, and competing priorities that delay responses to urgent needs. Current approaches to call demand management are largely reactive and shift based. They often overlook rapid fluctuations driven by patient acuity, prior call behaviour, room geography, time-of-day routines, and staffing conditions. This article proposes a smart hospital AI system for short-term prediction of nurse call button demand at the room or unit-zone level. The framework is conceptual and system oriented, with emphasis on how operational data streams could support proactive nursing workflow decisions. The system includes a patient acuity scoring module, call history learner, circadian pattern analyser, room-location spatial modeller, staffing-ratio adjuster, and real-time demand forecasting dashboard. Together, these components would translate fragmented operational signals into interpretable demand forecasts. The proposed system would support proactive allocation of nursing resources by identifying rooms or zones likely to generate elevated call demand. It could also guide anticipatory rounding, help reduce avoidable non-urgent calls, and support more balanced workload distribution. A demand-driven nursing workflow would move smart hospital operations beyond reactive response to patient-initiated requests. Predictive nurse call intelligence offers a pathway toward more responsive, equitable, and resilient inpatient care.

Smart hospitals increasingly combine artificial intelligence, real-time data streams, operational dashboards, and automated decision support to coordinate care delivery. These systems aim to improve hospital throughput, staff efficiency, resource use, and patient safety. This systematic review synthesised evidence on AI technologies used in smart hospital management from 2017 to 2023. The review focused on command centers, real-time workflow monitoring, operational dashboards, and automated decision support systems. A PRISMA 2020-compliant review process was used, including structured database searching, dual screening, and narrative synthesis. Extracted data covered study characteristics, AI methods, deployment maturity, data sources, integration patterns, and reported operational impact. The evidence base was concentrated on hospital command centers, predictive dashboards, and patient-flow analytics. Real-time workflow monitoring and automated decision support were less mature, and prospective evaluations of operational or clinical impact were uncommon. AI-enabled smart hospital management is technologically promising but remains unevenly implemented and weakly evaluated. Current evidence supports cautious adoption, local validation, and stronger evaluation designs before claims of sustained operational transformation are accepted.