Intelligent Sensing Technologies to Facilitate Clinical/Medical Decision Making
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 3
Special Issue Editors
2. Faculty of Health Sciences, Durban University of Technology, Durban 1334, South Africa
Interests: use of technology to protect athletes, replace/enhance impaired nervous systems, and facilitate restoration of function
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Interests: evaluation of sitting comfort and discomfort; signal measurement at the user–seat interface
Interests: data analysis; signal measurement and detection; medical information processing
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Special Issue Information
Dear Colleagues,
With the rapid evolution of intelligent systems and sensing technologies, healthcare is experiencing a paradigm shift in clinical decision making. This Special Issue focuses on the integration of sensing technologies—such as wearable biosensors, Internet of Things (IoT)-enabled sensing devices, medical imaging sensors, and ambient sensing systems—with intelligent algorithms (e.g., machine learning, deep learning, and regression analysis) to enhance the accuracy, timeliness, and personalization of medical decisions. Sensing technologies serve as the critical data acquisition infrastructure for clinical intelligence, capturing real-time physiological signals, pathological markers, and environmental cues that form the foundation of data-driven diagnosis, treatment planning, and prognostic assessment. By bridging sensing innovations and intelligent analytics, this issue showcases original research, reviews, and case studies that address key challenges (e.g., sensor data noise reduction, real-time processing, and clinical validation) and highlight transformative applications—from remote patient monitoring for chronic disease management to AI-driven diagnostic and prognostic models. We aim to gather interdisciplinary insights from engineering, computer science, and clinical medicine; integrate these technologies into healthcare workflows; and advance the synergy between sensing and artificial intelligence to revolutionize clinical practice.
Prof. Dr. Peter W. McCarthy
Dr. Vincenzo Cascioli
Prof. Dr. Zhuofu Liu
Guest Editors
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Keywords
- intelligent sensing
- clinical decision making
- wearable biosensors
- medical image processing
- machine learning in healthcare
- sensing-driven diagnosis
- real-time clinical monitoring
- artificial intelligence
- clinical sample analysis and validation
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