In recent years, how physiological and health-related data can be collected has dramatically expanded. Besides traditional clinical records and medical imaging, data are now gathered through wearable devices, IoT sensors, and other networked monitoring systems. These technologies enable continuous real-time collection of physiological and contextual data, going beyond traditional clinical environments. Effectively using these diverse data sources requires a reliable communication infrastructure and computing frameworks that support ongoing data integration and analysis across distributed systems.

In parallel, developments in network architectures, distributed computing (including cloud, fog, and edge environments), and data processing methods have expanded the possibilities for health management. These technologies allow information to be collected and analyzed closer to the source, support real-time monitoring, and facilitate applications ranging from personalized care to strategic planning. At the same time, the increasing interconnection of devices and services raises concerns regarding scalability, reliability, security, and interoperability, which must be addressed for such systems to function effectively in practice.

This Special Issue focuses on studies that address how networked systems and distributed computing frameworks can be applied to health data management and service delivery. We particularly welcome contributions that propose systematic solutions to practical problems, such as network and system design for real-time data transmission, distributed algorithms for large-scale health data processing, and methods for integrating heterogeneous data streams from wearable and IoT devices. Application-driven research that links communication technologies, computational methods, and healthcare management is especially encouraged.

Topics of Interest

Original research articles are invited in, but not limited to, the following areas:

• Artificial intelligence and large language model (LLM) techniques for early disease diagnosis, progression prediction, and risk assessment
• AI for multimodal healthcare data integration and representation learning
• Privacy-preserving AI and distributed learning methods, including federated learning, privacy-enhancing AI, and blockchain-based healthcare data sharing
• AI-enabled knowledge graphs and topology-based reasoning for healthcare decision support
• Natural language processing and biomedical text mining for clinical record automation, knowledge extraction, and decision recommendations
• AI integration with medical IoT devices and intelligent sensing for disease prediction and automated decision-making
• Real-world deployment of AI healthcare systems: integration, compliance, and case studies

Submission Guidelines