Academic Press Lightweight Secure and Intelligent Approaches for Medical Devices: Trends and Future Frontiers (Advances in ubiquitous sensing applications for healthcare)

Lightweight Secure and Intelligent Approaches for Medical Devices: Trends and Future Frontiers addresses the rapidly evolving intersection of artificial intelligence, Internet of Medical Things (IoMT), and edge computing within healthcare. This reference meets the critical demand for resource-efficient, secure, and trustworthy AI models tailored specifically for embedded medical devices operating under stringent constraints. By integrating advances in lightweight AI, medical device security, and intelligent data processing, the book fills a vital gap in current literature, offering researchers, engineers, and healthcare professionals a comprehensive resource to develop next-generation medical technologies. The book is organized into four parts, beginning with foundational concepts including the evolution of AI in healthcare devices, lightweight model design, and medical device security standards. It progresses to predictive intelligence and reliability, covering AI techniques for disease monitoring, device calibration, and personalized medicine. The security and privacy section explores network architectures, privacy-preserving AI, lightweight cryptography, and defenses against adversarial attacks. The final part presents emerging trends such as edge AI, digital twins, explainable AI, augmented and virtual reality applications, and quantum-safe security protocols. Each chapter combines theoretical insights with practical case studies and research perspectives to guide real-world deployment. Lightweight Secure and Intelligent Approaches for Medical Devices: Trends and Future Frontiers serves as an essential reference for researchers, medical device developers, cybersecurity professionals, and graduate students aiming to innovate secure and intelligent healthcare solutions in resource-constrained environments. Presents unified frameworks integrating AI and security in medical devices Explains lightweight AI model design optimized for low-power healthcare systems Demonstrates application-centric case studies with real-world medical device examples Highlights security mechanisms including lightweight cryptography and privacy preservation Discusses future frontiers such as quantum-safe protocols and generative AI