A ‘digital twin’ is a digital representation of either a physical object, person, or process. This virtual replica can be used to simulate behaviour to better understand exposure to different situations and conditions. The object being studied can be fitted with various sensors, each relating to different areas of functionality. The twin can be linked to the data received about different aspects of performance, such as temperature and energy output, and updated in real time as changes are made. Simulations can be run, and the abundance of data received through these can be used to help people and organisations make better decisions.
Digital twins are advantageous because they allow for product designs to be tested continually, as adjustments and redesigns are made, rather than having to test every physical prototype. For example, Emirates Team New Zealand, the champion sailing team, has generated a digital twin of sailing environments, boats and crew members. This has allowed them to test thousands of boat designs without needing to physically build them. Equally, the Mercedes Benz Group AG has developed customer twins that allow customers to ‘test drive’ a vehicle without getting behind the wheel, whilst the US Space Force is creating a digital twin of space, which includes replicas of extra-terrestrial bodies and satellites. These digital twin models are very sought after by companies across the globe, and their use is now being considered within the healthcare industry. The idea is in its early stages and although it is an exciting proposal, the feasibility of such an idea should be evaluated.
Furthermore, there is a growing need for the personalisation of disease treatments, and digital twinning would allow tailored models to be created for patients. A key advantage of this is that they could be continuously altered based on different parameters relating to health and lifestyle. The development of data driven mathematical models would allow for the creation of a ‘virtual patient’ or ‘in-silico-self’, with the healthy state being described in great detail. ‘Healthy’ or ‘normal’ could be described more explicitly, by comparing the records of individuals to the entire population. Disease states could be analysed in multiple data-dimensions, and factors such as genetic background, lifestyle and age can be considered. Natural variations amongst a population can make it tricky to define normality, but these factors could be mapped to form a statistical definition of the normal state. Once the “normal” has been defined for an individual, therapy could be seen as a way to either maintain or salvage this state. This quantitative approach to health would allow deviations to be spotted with greater ease, and continuous monitoring would allow illnesses to be prevented or treated more efficiently. Diagnoses can be more precise, and side effects of treatments may be predicted, to choose the most suitable one for the specific patient in question.
The development of data driven mathematical models would allow for the creation of a ‘virtual patient’ or ‘in-silico-self’, with the healthy state being described in great detail.
The application of digital twinning to medicine shows huge potential, but it also comes with a wealth of ethical and moral issues. One of the major issues is with regards to privacy. The collection and storage of such a copious amount of data raises concerns and is an idea which is unsettling to many. Perhaps education on the possibilities of digital twin technology would lead to a society that is more accepting of the idea, but apprehension is a barrier that needs to be overcome before the idea can become reality. Digital twins may also lead to more inequality, as the technology may not be accessible for everyone, exacerbating the already massive gap between the rich and the poor.
Another potential problem surrounds the idea of healthy people seeking help to prevent an issue which they have not yet encountered. Data gathered can be used to determine probabilities of developing certain diseases and ailments, which would allow patients to seek treatment for diseases which they have not yet developed but show high susceptibility for. Although this is of course helpful and advantageous to health, it poses a problem when looking from a financial point of view. It may be necessary to revise the definition of “therapy”, to determine what should be covered by public healthcare system. This decision will depend on whether such treatment is seen as daily care, therapy, or enhancement. It may be the case that treatment of this kind would only be covered to a certain extent, or only for certain conditions, but it is difficult to know where the line should be drawn.
Ethical questions also arise when considering potential predictions given by the twin. For example, would a patient have a right to either know, or to refrain from knowing, if their twin was to predict that they only had a month left to live? The legal rights of the twin itself may also pose a barrier. The issue of twinning is incredibly complex, and so there are many issues to be considered before the idea can be launched to the public.
Would a patient have a right to either know, or to refrain from knowing, if their twin was to predict that they only had a month left to live?
Despite the issues which digital twinning presents, there is definitely huge potential for the idea to be used to revolutionise the medical and healthcare industry via the personalisation of medicine. It is an experimental idea which is still being trialled and researched, but it is something which may be able provide a quantitative understanding of health and disease, and enable much more effective treatment.