Introduction

Connected Healthcare Delivery: Leveraging IoT and Machine Learning for Advanced Medical Solutions explains the convergence of the Internet of Things (IoT), machine learning, artificial intelligence, blockchain, edge computing, and emerging digital technologies in transforming modern healthcare delivery.

The book examines how connected healthcare ecosystems are enabling intelligent patient care and clinical decision-making by integrating technological innovation with practical healthcare applications. The volume provides valuable insights into the next generation of smart and data-driven medical systems.

The book is organised into sixteen chapters that cover the core technologies driving connected healthcare. It begins by introducing IoT-enabled healthcare applications and advances in connected medical devices, followed by discussions on interpretable AI techniques for stress detection using electroencephalogram (EEG) data and AI-driven predictive analytics in medical imaging.

The volume further examines wearable health technologies for continuous patient monitoring, deep learning applications for smarter health and driver safety, and blockchain-based solutions for secure, decentralised, and tamper-proof management of healthcare data.

The later chapters focus on the use of edge computing in medical imaging, blockchain-assisted disease diagnosis, advanced wearable technologies, AI-driven predictive healthcare models, and genomic analysis for precision drug discovery. Together, these contributions demonstrate how intelligent digital technologies are reshaping healthcare delivery by improving diagnostic accuracy, enhancing patient outcomes, strengthening data security, and enabling scalable, personalised medical solutions.

Key Features

• - Comprehensive coverage on the role of IoT, machine learning, AI, blockchain, and edge computing in healthcare with real-world applications of wearable devices for continuous patient monitoring and remote care.
• - AI-driven predictive analytics for medical imaging, disease diagnosis, and personalised medicine.
• - Blockchain-enabled frameworks for secure, decentralised healthcare data management.
• - Discussion of telemedicine, regulatory compliance, cybersecurity, and legal considerations.
• - Emerging perspectives on quantum computing and genomic analysis with practical insights into connected healthcare systems, intelligent diagnostics, and smart medical devices.

Target Readership :

Researchers, academics, postgraduate students, and professionals in healthcare informatics, biomedical engineering, artificial intelligence, Internet of Things, medical imaging, and computer science.