Abstract
A digital twin is a virtual model of a physical entity, with dynamic, bi-directional links between the physical entity and its corresponding twin in the digital domain. Digital twins are increasingly used today in different industry sectors. Applied to medicine and public health, digital twin technology can drive a much-needed radical transformation of traditional electronic health/medical records (focusing on individuals) and their aggregates (covering populations) to make them ready for a new era of precision (and accuracy) medicine and public health. Digital twins enable learning and discovering new knowledge, new hypothesis generation and testing, and in silico experiments and comparisons. They are poised to play a key role in formulating highly personalised treatments and interventions in the future. This paper provides an overview of the technology’s history and main concepts. A number of application examples of digital twins for personalised medicine, public health, and smart healthy cities are presented, followed by a brief discussion of the key technical and other challenges involved in such applications, including ethical issues that arise when digital twins are applied to model humans.
Highlights
The evolution of technology has transformed every industry from manufacturing to banking, and medicine is no exception to its wide-reaching impact
Developments in sensor technologies and wireless networks have pushed forward the applications of the Internet of Things (IoT), and contributed to the practical applications of digital twin technology. (IoT refers to internet-connected sensors and devices, embedded in everyday objects or attached to the human body, e.g., as wearables, that can collect, send, and receive data about the instrumented entities and/or their environment.) Thanks to IoT, digital twins can be created to collect much more realworld and real-time data from a wide range of sources, and can establish and maintain more comprehensive simulations of the physical entities, their functionality, and changes they undergo over time
Digital twin levels refer to the different levels of sophistication that apply to the rendering and execution of each of the above types
Summary
The evolution of technology has transformed every industry from manufacturing to banking, and medicine is no exception to its wide-reaching impact. Given today’s advanced scientific knowledge and extensive simulation capabilities, it is possible to construct digital twins for modelling different aspects or functions, such as the bio-physical systems or protein structures, of the human body. This would allow research questions about drug interactions, treatment effectiveness, procedure safety, and so on to be assessed with higher efficacy. (IoT refers to internet-connected sensors and devices, embedded in everyday objects or attached to the human body, e.g., as wearables, that can collect, send, and receive data about the instrumented entities and/or their environment.) Thanks to IoT, digital twins can be created to collect much more realworld and real-time data from a wide range of sources, and can establish and maintain more comprehensive simulations of the physical entities, their functionality, and changes they undergo over time. Sivalingam et al created a d2.igHitualmtwaninD-bigasiteadl fTrwaminesw: KorekyfCoropnrceedpitcstiavnedmPaointetnentiaanlce of offshore wind turbine power conveArtkerey[1f1e]a.tuMreoroef rdeicgeitnatllytw, iitnhs aiss tpheerimr deyanteamd iicnbiodtihreecrtidonisaclipmlainppesinagn. dDipgaitratlictuwlainrlsyairne hneoatlathmcaere.unidirectional map, digital shadow/snapshot, or simulation model of a physical real-world entity in the digital domain
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