A new AI model could predict a patient’s risk of more than 1,000 diseases a decade before they actually develop symptoms, according to new results.
The scientists who created the tool hope it could be ready for GPs and other doctors to use within 5 to 10 years, giving patients an early warning that they need to change their lifestyles or perhaps start medical intervention.
Ewan Birney, who led the team at the European Molecular Biology Laboratory in Cambridge, said it was “one of the most exciting bits of science I have been involved in – it’s really cool.”
The AI tool, called Delphi, was trained on the anonymised medical records of 400,000 people who have signed up to the UK Biobank research database.
It learned how their medical history changed over time, picking out patterns that were associated with later diseases.
The tool was then unleashed on the records of 1.9 million patients in the Danish National Patient Registry and was able to make “meaningful” predictions of the risk and timing of over 1,000 diseases, according to results published in the journal Nature.
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“By modelling how illnesses develop over time, we can start to explore when certain risks emerge and how best to plan early interventions, said Mr Birney.
“It’s a big step towards more personalised and preventive approaches to healthcare.”
The tool in effect gives a ‘health forecast’, much like a weather app.
Long-term predictions of a disease – or the chance of rain – are uncertain, though still useful. But short-term assessments of risk are more accurate.
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The research highlighted how the likelihood of disease can vary widely across the population.
Delphi showed that the risk of a heart attack in men aged between 60 and 65 ranged from 1 in 100 per year in some to 1 in 2,500 per year in others.
Women were less likely to have a heart attack on average, but there was a similar spread of risk.
The tool would need to be carefully assessed before it was rolled out for clinical use.
Moritz Gerstung, from the German Cancer Research Centre, which collaborated in the study, said some patients could become “fatalistic” after discovering they have a higher than average chance of a disease.
“There is an element of psychology that needs to be brought into an assessment of how such tools are used in the future,” he said.
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There is huge interest in the ability of AI to spot patterns in health data.
Pharmaceutical company AstraZeneca last year published results from an AI model that looked at data from routine GP visits, such as blood pressure checks and urine tests, as well as 3,000 proteins found in the blood.
It predicted the risk of 121 diseases with “exceptional” accuracy up to 20 years in advance, the researchers said.
