- Video report by ITV News Correspondent Geraint Vincent
Scientists are using artificial intelligence to successfully predict when people will fall ill from acute kidney injury (AKI), one of the biggest NHS killers.
Described as the "silent killer" because it can often be diagnosed late and is hard to predict, AKI involves sudden damage or decreased blood flow to the kidneys which is often treatable.
Without rapid treatment, patients can die, end up on dialysis or need a transplant.
The condition contributes to nearly 20% of all hospital admissions and costs the NHS £1.2 billion annually.
The new AI system from DeepMind Health can analyse up to 600,000 data points - such as blood tests, heart rate and blood pressure - and calculate whether someone will develop AKI up to 48 hours in advance.
The health and technology company's deep learning algorithm was applied retrospectively to records of more than 700,000 patients from the US Department of Veterans Affairs.
It was able to detect 55.8% of all inpatient episodes of AKI and 90.2% of all acute kidney injuries that required subsequent administration of dialysis.
Experts believe up to one in three deaths from AKI may be preventable if clinicians are able to intervene earlier and more effectively.
The app has also been found to reduce NHS costs by around £2,000 per hospital patient - from £11,772 to £9,761 for a patient with AKI.
They are hoping to pilot the technology in UK hospitals within the next 12-18 months.
Dr Dom King, the health lead for DeepMind Health, said: "This progress represents potentially a very significant change in how medicine is practised and care is delivered.
Dr King, previously a general surgeon, continued: "The current alerts are very simple and rules-based and don't really pick up the subtlety of those patients at the earlier signs of deterioration, so it really is mindblowing for me as a doctor that in some way these AI systems are almost doing what an expert physician does, which is to look at not one or two factors but to look at thousands of factors, from what time of the year it is to what part of the hospital the patient is in, and all of these things contribute in some way to their risk score.
"I think we've seen some real progress in the last couple of years in AI as applied to medical imaging, but this I think is potentially more impactful because it affects many more patients and it informs many more clinicians."