Medical Physics and Physiological Measurements: Intelligent Biosensing, Wearables and AI-Driven Quantified Health

Dear Colleagues,

Advances in medical physics, physiological sensing technologies, and data-centric computational sciences are accelerating the transition toward scalable, precision, and preventive healthcare. This Special Issue aims to attract high-impact, forward-looking contributions that address emerging scientific and technological challenges in the measurement, modeling, and intelligent interpretation of human physiological processes. We particularly encourage submissions that demonstrate strong methodological novelty, large-scale validation, translational relevance, and potential for clinical, industrial, or societal impact.

Topics of interest include next-generation biosignal acquisition and analysis, wearable and remote monitoring systems, digital biomarkers, and multimodal physiological data fusion. Special emphasis is placed on artificial intelligence approaches such as deep learning, foundation models for biosignals, federated and privacy-preserving learning, explainable and trustworthy AI, and real-time predictive analytics. Contributions integrating physics-based modeling, digital twin ecosystems, uncertainty quantification, and hybrid mechanistic–data-driven frameworks are highly welcomed, particularly when enabling personalized diagnosis, adaptive rehabilitation, or closed-loop therapeutic interventions.

We also invite studies addressing implementation science, interoperability, standardization of physiological measurement protocols, and deployment in real-world environments, including telemedicine, home-based care, sports performance, and occupational health. By bringing together interdisciplinary perspectives, this Special Issue aims to define future directions in intelligent physiological measurement and to promote reproducible, data-intensive research that can shape next-generation digital health and precision medicine paradigms.

Prof. Dr. Constantinos Koutsojannis
Guest Editor

Manuscript Submission Information

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