Other popular areas of research include liver and brain models, with the European Commission investing €38M in an EBRAINS 2.0 project which will push forward digital twin approaches through modelling and simulation.

One thing to note is that each organ often has unique complexities, which can impact development, causing startups and larger healthtech vendors to be less likely to invest in multiple development projects at a time!


Some startups are approaching the physiological space more aggressively. Qbio, for example, is ingesting any type of information about the human body. This reportedly includes genetics, biochemistry, imaging, quantitative anatomical data, vitals, wearable data, medical, family and social history and more. Its Gemini product automatically structures this data and is intending to provide its customers personalised health forecasts with recommendations for change. This is no small feat; the company is concurrently developing a scanner, digital twin, and foundation model that will be integrated into a turnkey offering. With ~35 staff according to its website, it is likely that this will take some time.

As is frequent in healthcare, the applications targeting the "lowest hanging fruit" often contain the most saturated clinical environments. But are there any common barriers vendors face, which can be used to assess potential?

Fog on the glass: What are the barriers?
Because digital twins are in essence the same structure, shown previously in Figure 1, there are four common barriers vendors will face when trying to develop and commercialise solutions:

Accurate Models Require High Quality Data — Which is in Short Supply
Software designed to predict outcomes requires vast amounts of high-quality longitudinal data, which is not commonly available today across healthcare and life sciences.

For operational purposes this can often be collected prospectively using business analytics tools, which requires significant investment but is not impossible. Similarly, cell and tissue studies may be able to take advantage of high throughput processing used in drug development to accumulate data quickly.

But where clinical data is concerned, there is a lot more difficulty, as it is often unstructured and hard to process in large volumes. Structuring this data into machine-readable content can often require significant effort. For some rare diseases there will also be an acute shortage of data due to the smaller populations of patients receiving care.

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