Cognitive Internet of Things in Biomedical Devices, Gebunden

Klappentext

Bridging the gap between theory and real-world deployment, this essential guide reveals how cognitive IoT is moving healthcare past passive data tracking to usher in a new era of autonomous, real-time, and life-saving patient care.

The convergence of artificial intelligence, machine learning, and the Internet of Things is fundamentally reshaping healthcare into an intelligent, adaptive, and patient-centric ecosystem. Traditional IoT systems in healthcare have largely functioned as passive data collectors, transmitting vital signs and patient information to healthcare providers. Cognitive IoT, however, introduces self-learning and autonomous decision-making capabilities that enable biomedical devices to analyze complex physiological patterns, predict potential health anomalies, and adapt interventions in real time. From smart prosthetics that respond to neural signals to AI-driven implants that regulate bodily functions, cognitive IoT is redefining the boundaries of medical possibilities. The book explores how this powerful synergy transcends incremental progress to fundamentally transform the way we monitor, diagnose, and manage patient health. Bridging the gap between theoretical foundations and practical applications, this volume covers core cognitive IoT concepts and cutting-edge biomedical innovations demonstrating autonomous diagnostics, intelligent drug delivery, and AI-powered wearables. It examines critical challenges, including federated learning for privacy preservation, cybersecurity vulnerabilities, and ethical considerations in AI-driven medical interventions. This book combines foundational knowledge, applied techniques, and case studies to serve as a comprehensive resource on how cognitive IoT is redefining the healthcare landscape and paving the way for a new era of responsive and intelligent patient care.

Readers will find the volume:

• Provides a comprehensive framework for integrating Cognitive IoT, specifically for biomedical devices;
• Unites advanced AI and machine learning theory with practical, clinically relevant applications in smart healthcare;
• Covers emerging trends like explainable AI for clinical trust and adaptive, patient-centric systems;
• Serves as a foundational text for both educational courses and industry research and development, featuring case studies and practical insights.

Audience

Researchers, academics, clinical technologists, hospital IT managers, data scientists, and policymakers in biomedical engineering, computer science, and healthcare informatics.