By Alexandra Bream
Healthcare is innovating at such an incredible speed that it can be difficult to keep track of and assess which startups, products and innovations are most likely to impact healthcare and healthcare businesses in the short-midterm. Our forthcoming project, "Digital Precision Medicine – World – 2024", will offer insight into how investors and healthcare companies should approach and prioritize different emerging markets, including digital twin software which is the focus of this insight. It will also evaluate the emerging digital technologies driving precision medicine in healthcare, including genomics, artificial intelligence and digital therapeutics among other tools.
However, when considering cutting-edge technology, the first challenge is how to define a new product type. We therefore begin by asking: what is a digital twin?
The Framework
Digital twins are replicas produced in a digital environment used to simulate effects. When applied to healthcare settings they come tailored to many specific situations, and can be modeled on cells, tissues, organs, patients, medical devices, care units, departments, entire hospitals, or wider healthcare ecosystems.
The components of a digital twin are summarised in figure 1 below:
The software component of a digital twin can employ a range of analysis techniques, with some being rather simple and others advanced. Computer vision, natural language processing, machine learning, deep learning, and generative AI are all examples of techniques that can be used to create a model. Each model must be specifically written for a unique purpose using individual training data and so in practice, due to the substantial number of potential applications and data types across healthcare, these models vary considerably today.
Some potential benefits to using digital twins in healthcare can include:
– Inform and improve patient care by simulating the effects of interventions on specific organs or individuals.
– Enable more effective patient engagement through allowing patients to interact with models regarding lifestyle changes.
– Predict and minimise adverse events in vast populations, such as the likelihood of pandemics, or even individual events like heart attacks.
