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Digital Twins for Healthcare: Design, Challenges and Solutions establishes the state-of-art in the specification, design, creation, deployment and exploitation of digital twins' technologies for healthcare and wellbeing. A digital twin is a digital replication of a living or non-living physical entity. When data is transmitted seamlessly, it bridges the physical and virtual worlds, thus allowing the virtual entity to exist simultaneously with the physical entity. A digital twin facilitates the means to understand, monitor, and optimize the functions of the physical entity and provide continuous feedback. It can be used to improve citizens' quality of life and wellbeing in smart cities and the virtualization of industrial processes.
The Digital Twin (DT) technology has gained success in the production industry, and it is now getting attention in the domain of health and well-being. The healthcare DT models require Artificial Intelligence (AI) capabilities related to various health conditions, including early warnings of a patient's critical condition, classification of diabetes risk, heart risk, and prediction for preparedness for hospital management. In addition, DT is adopting AI to increase the scope and impact of healthcare applications. However, the AI-based DT models in healthcare are still less explored, because DT in health and well-being is an emerging field. Therefore, this chapter aims to provide an overview of AI-based models for health and well-being, outline the current technological advances, and consider such models in the literature. The AI in healthcare is not new. To improve the quality of human life, AI is already in practice for individuals' well-being. Several technologies like the Internet of Things (IoT) and Cyber-Physical Systems (CPS) have adopted AI for decision-making and prediction. The DT is no exception.
Presents the fundamentals of digital twin technology in healthcare
Facilitates new approaches for healthcare industry
Explores different use cases of digital twins in healthcare