Digital twins and AI in health

The digitization in the health sector and the representation and access to medical data is a very timely and much discussed topic in Germany and beyond. Even if we set aside the legal discussions about accessibility to patient data and the EU’s general data protection regulation (GDPR), there are many challenges in aligning the representation of medical data across institutions and the secure and authorized access to patient data. The interest in this highly sensitive medical data is driven by the promise of machine learning and data analytics, namely that in all this data there are insights and knowl-edge that can bring benefits to patients through better diagnosis and more personalized treatments and drugs. This chapter wants to shed some light on medical digital twins as an emerging form of medical data representation, and the benefits and pitfalls of using AI on and with these digital twins. We will start with an introduction of medical digital twins (MDTs), before we look at cohort studies and current applications of MDTs. The following sections will then focus on AI methods that are applied to MDTs and how medical decision making can benefit from this. Finally, we will cover ethical consider-ations in this area.