Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm

Abstract

Personalized medicine uses fine grained information on individual persons, to pinpoint deviations from the normal. ‘Digital Twins’ in engineering provide a conceptual framework to analyze these emerging data-driven health care practices, as well as their conceptual and ethical implications for therapy, preventative care and human enhancement. Digital Twins stand for a specific engineering paradigm, where individual physical artifacts are paired with digital models that dynamically reflects the status of those artifacts. When applied to persons, Digital Twins are an emerging technology that builds on in silico representations of an individual that dynamically reflect molecular status, physiological status and life style over time. We use Digital Twins as the hypothesis that one would be in the possession of very detailed bio-physical and lifestyle information of a person over time. This perspective redefines the concept of ‘normality’ or ‘health,’ as a set of patterns that are regular for a particular individual, against the backdrop of patterns observed in the population. This perspective also will impact what is considered therapy and what is enhancement, as can be illustrated with the cases of the ‘asymptomatic ill’ and life extension via anti-aging medicine. These changes are the consequence of how meaning is derived, in case measurement data is available. Moral distinctions namely may be based on patterns found in these data and the meanings that are grafted on these patterns. Ethical and societal implications of Digital Twins are explored. Digital Twins imply a data-driven approach to health care. This approach has the potential to deliver significant societal benefits, and can function as a social equalizer, by allowing for effective equalizing enhancement interventions. It can as well though be a driver for inequality, given the fact that a Digital Twin might not be an accessible technology for everyone, and given the fact that patterns identified across a population of Digital Twins can lead to segmentation and discrimination. This duality calls for governance as this emerging technology matures, including measures that ensure transparency of data usage and derived benefits, and data privacy.

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

“…In [9], the authors give insight into how a digital twin framework for remote surgery might look like. Bruynseels et al [10] projects digital twin as an emerging technology building silico representations of an individual that dynamically reflects molecular status, physiological status and life style over time. Zhang et al [11] gives an overview of the latest developments in organ-on-a-chip (OOC) engineering.…”

Multiscale Modeling Meets Machine Learning: What Can We Learn?

“…This will help in creating digital twins [72], models that incorporate both machine learning and multiscale modeling, for an organ system or a disease process in an individual patient. Using digital twins, we can identify promising therapies before trying them on the one patient [15]. As multiscale modeling attempts to leverage experimental data to gain understanding, machine learning provides a tool to preprocess these data, to automate the construction of models, and to analyze model output [132].…”

‘Without data, you’re just another person with an opinion’

“…As a result of predictive analytics, we now have the ability to include new data sets that enable personalized medicine. Through the creation of 'digital twins' we can add more information into data models to predict the future [51]. In addition to classic clinical information (i.e., laboratory results, medical imaging), we can now process genomics, behavioral, or social determinants to enable personalized medicine schemes.…”