Introduction
The proposed thematic issue focuses on the development of AI-Driven Sustainable Edge Intelligence for Next-Generation Healthcare Systems. With the rapid expansion of Artificial Intelligence (AI), Internet of Medical Things (IoMT), and decentralized computing environments, healthcare systems are increasingly adopting intelligent and distributed architectures capable of delivering real-time medical insights while preserving privacy and optimizing energy consumption.
This thematic issue aims to bring together cutting-edge research that integrates AI with edge computing to support low-latency, energy-efficient, secure, and scalable healthcare applications. The scope includes both theoretical advancements and practical implementations that leverage edge intelligence for medical diagnostics, clinical decision support, remote patient monitoring, and smart hospital infrastructures.
Particular emphasis will be placed on sustainable AI methodologies, privacy-preserving learning models, and collaborative edge–cloud architectures designed to address challenges related to data security, computational efficiency, and real-time healthcare analytics. Contributions exploring novel frameworks, system architectures, algorithms, and real-world deployment scenarios will be welcomed.
The issue will provide an interdisciplinary platform for researchers, engineers, and healthcare technologists working at the intersection of Artificial Intelligence, edge computing, communication systems, and digital healthcare innovation, with the goal of advancing intelligent and sustainable healthcare ecosystems.
Keywords
Artificial Intelligence in Healthcare, Edge Computing; Sustainable AI, Green Computing, Internet of Medical Things (IoMT), Federated Learning, Energy-Efficient Deep Learning, Privacy Preserving Healthcare Analytics, Edge–Cloud Collaboration, Real-Time Medical Data Processing, Smart Healthcare Systems, Explainable AI
Sub-topics
• Sustainable and Green AI models for healthcare applications • Edge intelligence frameworks for Internet of Medical Things (IoMT) environments
• Federated learning and privacy-preserving distributed AI for medical data
• Edge–cloud collaborative architectures for healthcare analytics • Energy-efficient deep learning models for medical imaging and biosignal processing
• Secure and trustworthy AI frameworks for clinical systems
• Resource optimization and intelligent orchestration in smart hospital infrastructures
• AI-enabled real-time healthcare monitoring and telemedicine systems
• Low-latency AI inference for emergency and critical care applications
• Explainable and trustworthy AI for clinical decision support systems
• Blockchain and secure data-sharing frameworks in decentralized healthcare
• AI-driven predictive analytics for personalized and preventive healthcare
• Lightweight AI algorithms for wearable and low-power medical devices
• Edge-enabled remote patient monitoring systems
• AI-powered healthcare data management and interoperability frameworks
