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This is the first of a six-part series on how AI is changing medical research and treatments.
The heart in front of me beats and moves like a human organ, but has no blood flowing through it, nor does it live in a human body. It’s a computer-generated heart, or digital twin, used to test implantable cardiovascular devices, like stents, and prosthetic valves that, once confirmed they are safe, will eventually be used on real people.
But the heart’s creators, Adsilico, have gone beyond just creating one accurate model. Using artificial intelligence and huge amounts of data, they have created multiple different hearts. These AI-generated synthetic hearts can be made to reflect not just biological attributes like weight, age, gender and blood pressure, but health conditions and ethnic backgrounds.
As these differences often aren’t represented in clinical data, digital twin hearts can help device manufacturers conduct trials across more diverse populations than they could with human trials, or trials involving just digital twins without AI.
This allows us to capture the full diversity of patient anatomies and physiological responses, which is not possible using conventional methods. This use of AI to enhance device testing leads to the development of devices that are more inclusive and safer.
In 2018 an investigation by the International Consortium of Investigative Journalists revealed that 83,000 deaths and over 1.7 million injuries were caused by medical devices.
Ms Macpherson hopes that AI powered digital twins can cut down those numbers.
To really make these devices safer, you need to test them more thoroughly, and it isn’t feasible to do that in a clinical trial environment due to the expense of it, so you want to be able to use the computer-generated version, to make sure that whatever you’re doing, you’ve tested it as thoroughly as possible before you test it on a human.
Even a fraction of those deaths – and the associated lawsuits – could have been avoided with more thorough testing. You could take the same [virtual] heart and you could test under low or high blood pressure, or against different disease progression, to see whether that affects the device in any way.
Using biological data from real people, Sanofi creates AI-based simulated patients – not actual clones of specific individuals – that can be interspersed across the control and placebo groups within the trial.
Using digital twins could mean big savings for drugs firms says Matt Truppo.
There is still a lot to do. Many of the diseases we are now trying to impact are highly complex. This is where tools like AI come in. Powering the next generation of digital twins with accurate AI models of complex human biology is the next frontier.
Digital twins might have weaknesses though, says Charlie Paterson, an associate partner at PA Consulting and a former NHS service manager. He points out that the twins are only as good as the data that they are trained on.
[Due to] aged data collection methods, and low representation of marginalised populations, we could end up in a position where we could still be introducing some of those biases when we’re programming virtual recreations of individuals.
Working with limited legacy data to train its AI is an issue Sanofi is aware of and working to resolve. To fill gaps in its internal data sets – made up of millions of data points from the thousands of patients that undergo its trials each year – it sources data from third parties, like electronic health records and biobanks.
Back at Adsilico, Ms Macpherson is hopeful that one day AI digital twin technology will eliminate animal testing from clinical trials, which is still currently considered an essential part of the drug and device testing process.
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