Smart Healthcare 4.0: AI, Quantum Computing, and Real-Time Biomedical Monitoring

Dear Colleagues,

The assimilation of quantum computing, artificial intelligence (AI), and edge computing into healthcare 4.0 will transform real-time biomedical monitoring, diagnostics, and clinical decision-making. Conventional healthcare systems face challenges in processing vast physiological datasets, handling non-stationary biosignals, and ensuring real-time responsiveness in critical applications such as neurophysiological monitoring, cardiovascular diagnostics, and anesthesia depth estimation. Quantum-accelerated AI frameworks offer a paradigm shift by enhancing biomedical signal processing, feature extraction, and classification through quantum variational circuits, hybrid quantum-classical models, and quantum-enhanced deep learning.

This Special Issue invites pioneering research on quantum-driven AI methodologies for real-time biomedical monitoring, covering areas such as quantum-assisted physiological sensor (EEG and ECG, etc.) analytics, variational quantum circuits for biosignal denoising, quantum-enhanced Fourier transforms for spectral decomposition and quantum Boltzmann machines for disease classification. Additionally, the role of IoT-enabled intelligent sensors, federated learning, fog computing, and edge AI will be explored to develop autonomous, self-adaptive, and energy-efficient healthcare monitoring solutions. By harnessing quantum speedup and AI-driven predictive analytics, this research aims to establish precision-driven, real-time, and adaptive biomedical monitoring systems, revolutionizing healthcare delivery in the era of Smart Healthcare 4.0. The scope of this Special Issue includes, but is not limited to, the following topics:

• Monitoring physiological and pathological signals;
• Personalized healthcare of wearable sensing systems;
• Gerontechnologies for assistive support systems;
• Telemedicine and disease monitoring;
• Biomedical adaptative control systems;
• Edge computing and distributed AI powered medical systems;
• Large scale data processing for health informatics.

Prof. Dr. Jiann-Shing Shieh
Dr. Shou-Zen Fan
Dr. Faiyaz Doctor
Guest Editors

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