AI-Driven Intelligent Systems in Energy, Healthcare, and Beyond

Dear Colleagues,

Artificial intelligence (AI), in conjunction with the Internet of Things (IoT) and robotics, is advancing intelligent, autonomous, and data-centric solutions across various sectors. Notably, energy and healthcare have emerged as two of the most impactful application domains. In the energy domain, the convergence of AI and IoT is modernizing infrastructure, optimizing consumption, and supporting sustainability goals. Real-time data from smart grids and renewable sources, when combined with AI-driven analytics, enable predictive maintenance, intelligent control, and demand-side optimization, contributing to more resilient and efficient energy systems. In healthcare, AI and robotics are reshaping diagnostics, treatment, and patient care through applications such as AI-driven imaging, wearable monitoring, robot-assisted surgeries, and autonomous rehabilitation. Advances in signal and image processing are improving diagnostic accuracy and enabling personalized healthcare delivery. These advancements are further empowered by progress in high-performance AI systems, including model compression, parallel computing, and deployment on edge and embedded platforms. Lifelong learning and self-adaptive algorithms enhance the responsiveness and robustness of AI systems in dynamic environments. This Special Issue aims to bring together cutting-edge research at the intersection of AI, high-performance computing, and robotics, with a focus on real-world intelligent applications in energy, healthcare, and beyond.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• AI-enabled energy optimization, including intelligent demand response, load balancing, and consumption forecasting;
• IoT-based monitoring and predictive maintenance for energy systems and related domains;
• Secure and resilient AI- and IoT-enabled cyber–physical energy systems;
• High-performance AI architectures, including model compression, parallelization, and deployment on heterogeneous hardware;
• Edge and embedded AI for low-latency, energy-efficient intelligence in constrained environments;
• AI-powered robotics for domain-specific applications such as healthcare and industrial automation;
• Signal and image processing for diagnostics, monitoring, and rehabilitation.

Prof. Dr. Lin Meng
Dr. Xiangbo Kong
Dr. Hengyi Li
Guest Editors

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