Integrating Embedded Systems and Neural Network Models for Real-Time Clinical Communication and Smart Healthcare Interoperability

Abstract

The convergence of embedded systems and neural network models is reshaping real-time clinical communication and enabling a new generation of interoperable smart healthcare infrastructures. Embedded systems provide low-latency, energyefficient platforms for continuous physiological sensing, device control, and edge-level data acquisition, while neural networks offer robust capabilities for pattern recognition, predictive analytics, and adaptive decision support in complex clinical environments. This review examines how the integration of these technologies supports real-time communication across heterogeneous medical devices, electronic health records, and clinical decision-support systems. Emphasis is placed on edge and fog computing architectures that reduce reliance on centralized cloud processing, thereby improving responsiveness, data privacy, and system resilience. The paper synthesizes recent advances in neural network deployment on resource-constrained embedded hardware, including model compression, on-device learning, and hardware-aware optimization techniques. Interoperability challenges are analyzed in the context of healthcare communication standards, data heterogeneity, and cybersecurity requirements. By consolidating architectural frameworks, deployment strategies, and clinical use cases, this review provides a structured perspective on how embedded intelligence can enhance situational awareness, care coordination, and patient outcomes. The study concludes by identifying research gaps and future directions for scalable, secure, and interoperable smart healthcare ecosystems.