Digital Twins: From Personalised Medicine to Precision Public Health

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.

Evaluating Translational Methods for Personalized Medicine—A Scoping Review

“…Departing from different patient-specific parameters, they can capture inter- and intra-patient variability [ 21 ]. Despite the enormous opportunities offered by these models, a full-scale adaption of patient-specific implementation is still far from reality; the main limitation relates to the prediction accuracy, which depends on the quality and quantity of the input data, and the lack of standards and model validation [ 65 ]. The current pros and cons are summarized in Table 3 .…”

Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence

“…Revolutionizes the healthcare sector by enhancing clinical procedures and hospital management, with digital monitoring and advanced modeling of systems of the human body. These tools are very helpful to researchers in the study of various diseases, novel drug formulas, and medical devices [22][23][24][25][26][27].…”

Health Technology Assessment for In Silico Medicine: Social, Ethical and Legal Aspects

“…Rather, in silico medicine is a promising approach to reduce the above-reported tensions. Several research groups are working to evaluate the capacity of the models to represent reality and to assess their impact on healthcare costs and on the duration of pre-clinical and clinical development [5,26,27].…”

“…Over the past decade, computer modeling and simulation (CM&S) technologies have increasingly been applied in disease prevention, diagnosis and treatment by simulating real biological processes in a virtual environment [1]. These technologies, also referred as in silico medicine or computational medicine, can, with varying levels of autonomy, make predictions, recommendations, or decisions influencing real or virtual environments [2] and have four targets: (a) doctors, through patient-specific models to support medical decisions within the precision medicine paradigm (digital twins or digital avatars, when a dynamic pairing is done with the modeled physical entity); (b) citizens, through easier and more pervasive access to their personal data-including those collected by wearable and environmental sensors-and personalized health status forecasting, providing advice for self-management; (c) decision-makers, by modeling person-to-person interactions and factors affecting health at population level (e.g., to prevent and manage epidemics) within the precision public health paradigm; and (d) research organizations/companies, through modeling virtual patient populations applied to reduce, refine, and partially replace pre-clinical and clinical assessment of medical technologies [3][4][5]. Two kinds of models can be used and also combined in hybrid solutions: (a) mechanistic models that incorporate scientific knowledge in biophysics, biochemistry, and physiology and are based on cause-effect relationships, and (b) phenomenological models that start from sufficiently numerous empirical observations and use statistics, system identification methods, or artificial intelligence (AI) to develop predictors without any causal assumption [6].…”

“…CM&S solutions can also be a combination of technologies. For example, digital twins can integrate modern technologies such as smart sensors (also as an application of the Internet of Things), data analytics, and AI to embed learning, intelligence, and autonomy [5,7,8].…”