Digital Twins constitute a class of innovative data-intensive network-based computing solutions based on real-time interactive simulations of physical systems—Physical Twins—in multiple domains, representing and predicting the states of complex systems (from bacteria to cities). However, the variety of Digital Twins proposed in literature makes it difficult to shape a general definition for this class of computational tools (possibly merging with phygital systems). On the other hand, this is a sign of the versatility of Digital Twins, in both their own capability to solve heterogeneous problems in real world and to inspire novel approaches to handle the uncertainty in complex systems. Such advantages are also explored in fields like healthcare, especially considering domains of Digital Health and mHealth. Indeed, major efforts in research are pushing the methodological and technological boundaries of biomedical sciences, translational research, and precision medicine. Such efforts should be pursued for implementing Digital Twins helping, for instance, the human-centric optimization of the resources allocation in healthcare settings (hospitals, homes, and cities). This chapter will present the opportunity and the ethical issues of using these systems for foreseeing the future states of a person or a population (a need that certainly was highlighted during the COVID-19 pandemic) or a system including a medical device and its user.
Barresi, G., Gaggioli, A., Sternini, F., Ravizza, A., Pacchierotti, C., De Michieli, L., Digital Twins and Healthcare: Quick Overview and Human-Centric Perspectives, in Scataglini, S., Imbesi, S., Margues, G. (ed.), mHealth and Human-Centered Design Towards Enhanced Health, Care, and Well-being, Springer Science and Business Media Deutschland GmbH, Berlino 2023: <<STUDIES IN BIG DATA>>, 120 57- 78. 10.1007/978-981-99-3989-3_4 [https://hdl.handle.net/10807/275474]
Digital Twins and Healthcare: Quick Overview and Human-Centric Perspectives
Barresi, Giacinto
Primo
;Gaggioli, Andrea;
2023
Abstract
Digital Twins constitute a class of innovative data-intensive network-based computing solutions based on real-time interactive simulations of physical systems—Physical Twins—in multiple domains, representing and predicting the states of complex systems (from bacteria to cities). However, the variety of Digital Twins proposed in literature makes it difficult to shape a general definition for this class of computational tools (possibly merging with phygital systems). On the other hand, this is a sign of the versatility of Digital Twins, in both their own capability to solve heterogeneous problems in real world and to inspire novel approaches to handle the uncertainty in complex systems. Such advantages are also explored in fields like healthcare, especially considering domains of Digital Health and mHealth. Indeed, major efforts in research are pushing the methodological and technological boundaries of biomedical sciences, translational research, and precision medicine. Such efforts should be pursued for implementing Digital Twins helping, for instance, the human-centric optimization of the resources allocation in healthcare settings (hospitals, homes, and cities). This chapter will present the opportunity and the ethical issues of using these systems for foreseeing the future states of a person or a population (a need that certainly was highlighted during the COVID-19 pandemic) or a system including a medical device and its user.
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