'Digital Twin' in medicine

Category: product safety Industry: healthcare Author: Year:

Patient and medical device as simulation

The 'digital twin' of a patient can help to reduce the risks associated with a given type of treatment and avoid unnecessary therapies; the digital twin of a medical device aims to ensure more product safety. 

Artificial intelligence (AI) is gaining ground in the health industry too. It can prevent operations from being carried out when they are not necessary or medication from being taken when it is contra-indicated or incorrect. The use of medical devices can also be made safer. This is all possible with the so-called 'digital twin', which simulates either the patient or a medical device on the computer.

One thing for sure is needed to create the digital twin 'patient': enormous quantities of health data. In their simplest form, these come from fitness watches or trackers; a far more complex way of gathering them is via genetic analysis. Once the information gathered has been fed in, a digital image of the patient, accurate in every detail, is recreated as far as possible on the computer. This makes it possible, for example, to simulate the consequences of an operation or the effects of taking certain medication on the individual human organism, calculate the chances of success and risks of a given type of treatment, and forecast the course of healing. Furthermore, operations that might have been decided against without the deployment of a digital twin on account of what was thought to be a high risk can perhaps be performed after all under certain circumstances, if the risk cannot be confirmed given the existing data status.

Having said that, the deployment of a digital twin is also of special interest to manufacturers of medical devices, since the digital replica of a product can help its manufacturer to fulfil his obligations under product safety and product liability law. On the one hand, this is made possible by insights gained early on in digital test runs prior to introduction on the market, in which the behaviour of the medical device when it is used in accordance with its intended purpose is simulated. On the other, the data can be collected by medical devices which are already in the field, and analysed. In this way valuable inferences can be drawn concerning the design of future product models. 

Conclusion

The digital twin thus has the potential to further digitalise the health industry and thus transform it, saving costs (for example those of unnecessary operations), and improving the quality of patients' lives. Whilst in the past there was, for example, often no alternative to an operation when a malfunction occurred on a heart pacemaker, this can now be avoided via the deployment of a digital twin using data that already exist. Furthermore, a digital twin can also make it easier for a manufacturer to provide proof of sufficient testing prior to introduction on the market. Having said that, the relevance of the requirements of data protection law should not be underestimated here.

[November 2018]