Through comprehensive insights and real-world case studies, this book features in-depth knowledge of key concepts relating to optimizing biomedical IoMT systems. Biomedical Internet of Medical Things is a technological paradigm encompassing a range of technologies that enable machines to mimic human intelligence. Machine and deep learning algorithms facilitate self-learning for the discovery of hidden patterns, associations, and risks from voluminous datasets. Computer vision and natural language processing are prominent applications of AI, allowing machines to see and understand the world in ways previously only possible for humans. In healthcare, generative techniques can analyze large and complex datasets from wearable sensors, identifying patterns and trends that can aid in detecting, diagnosing, and monitoring chronic diseases. This book comprehensively consolidates the latest technologies, groundbreaking research, and practical applications of computational intelligence in biomedical IoMT, with a strong emphasis on optimizing healthcare information systems. Readers will find the volume: Explores the transformative role of computational intelligence in the Internet of Medical Things (IoMT), demonstrating how intelligent systems enhance healthcare efficiency, accuracy, and patient-centric solutions at various scales; Examines key computational intelligence techniques and algorithms used in modern biomedical IoMT applications, emphasizing their impact on real-time diagnostics, personalized treatment, and remote patient monitoring; Highlights the evolution of AI-driven paradigms in biomedical IoMT, showcasing their role in predictive analytics, automated decision-making, and adaptive healthcare systems; Investigates the integration of trust management and advanced cybersecurity frameworks in intelligent healthcare networks. Audience Academics, research scholars, and industry professionals in the fields of mathematics, computer science, information technology, and health science.
