Healthcare 5.0

Healthcare 5.0: Applications of Artificial Intelligence, Machine Learning, IoMT, and Big Data addresses the urgent need for innovation in today’s complex healthcare data landscape, characterized by pandemics, aging populations, and escalating chronic conditions. This book introduces the concept of ‘Healthcare 5.0’ as an interconnected, data-driven, and patient-centric framework, where advanced technologies-such as AI, ML, IoMT, Big Data, and Large Language Models (LLMs)-converge to optimize care, streamline operations, and deliver personalized, predictive solutions that meet real-world challenges. Comprising six comprehensive sections, the book moves from core AI applications in electronic health records, drug discovery, data management, and privacy, through cutting-edge big data analytics for precise disease forecasting and diagnosis. It explores new research advances in the Internet of Medical Things including connected device architectures and their fusion with AI for dynamic decision-making. The third section focuses on data analytics in telemedicine, remote care, system usability, and integration in Healthcare 5.0. The personalized healthcare section details analysis and applications in AI- and IoT-powered assistance, and real-time monitoring. The last section explores the development of LLMs and their applications in medical imaging, clinical decision support, predictive analytics, system architectures, as well as the ethical challenges of their deployment in healthcare. Healthcare 5.0: Applications of Artificial Intelligence, Machine Learning, IoMT, and Big Data serves as an essential resource for graduate students, researchers, and engineers in computer science, data science, and biomedical informatics. It bridges theory and practical application, offering interdisciplinary insights, foundational background, detailed case studies, and guidance on navigating the next generation of healthcare data systems. Whether for research or real-world innovation, readers gain the tools to design, analyze, and implement intelligent healthcare data solutions for a rapidly evolving digital era.

• Delivers practical frameworks for integrating AI, ML, Big Data, and IoMT into modern healthcare data systems
• Explores predictive data analytics for improved patient outcomes in personalized medicine
• Examines implementation challenges, data management, and security solutions for healthcare technology adoption
• Highlights advancements in data architecture for telemedicine and remote care, enhancing accessibility and efficiency