AI detects heart failure and valve disease from a standard ECG in under two seconds
New CapabilitiesImperial College London algorithm trained on 10.6 million ECGs could fast-track patients who wait months for an ultrasound
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Overview
Updated 1 hour agoAn AI tool developed at Imperial College London can spot heart failure and heart valve disease from a standard electrocardiogram (ECG), a test that has been in clinical use for a century. In a trial of 67,000 US patients, it flagged up to 81 percent of heart failure cases and 90 percent of valve disease cases — in under two seconds.
Today those conditions are diagnosed with an echocardiogram, an ultrasound scan that patients in many health systems wait months to receive. The tool doesn't make the diagnosis; it decides who gets a scan first. The team is now testing it on 590 NHS patients in London and Bristol, and says routine use is possible within two years.
Why it matters
If it clears trials, the test run a billion times a year could fast-track the heart patients who need treatment most.
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People Involved
Organizations Involved
The London research university that developed and is now validating the AI ECG tool across NHS patients.
The UK's largest independent funder of cardiovascular disease research, which financed the Imperial College team.
The professional body whose annual congress in Munich hosted the AI ECG presentation, August 28-31, 2026.
Timeline
August 2026 September 2026
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Optimist Daily highlights screening potential
Today Media coverageCoverage emphasizes the tool's role in flagging high-risk patients for faster echocardiograms.
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British Heart Foundation publishes trial details
StatementBHF releases technical breakdown, AUC scores, and the 590-patient NHS trial plan.
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Guardian reports 'superhuman AI' tool
Media coverageFirst major coverage describes the AI spotting heart disease in less than two seconds.
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ESC Congress opens in Munich
ConferenceImperial College team presents AI ECG results to thousands of cardiology delegates.
Historical Context
3 moments from history that rhyme with this story — and how they unfolded.
Apple Heart Study (2017-2019)
Stanford Medicine worked with Apple to enroll 419,000 participants in a study using the Apple Watch's optical heart sensor to detect irregular pulses suggesting atrial fibrillation, a common heart rhythm disorder. The research showed a consumer wearable could flag a condition many people didn't know they had.
The study reported a 0.84 percent rate of irregular pulse notifications, with most flagged participants confirmed to have atrial fibrillation.
Apple's ECG app gained FDA clearance in 2018, demonstrating how AI-driven screening moves from research to regulated devices.
Shows the typical path an AI screening tool travels: research validation, regulatory clearance, then deployment. The Imperial ECG tool is at the same early stage Apple's watch was in 2017.
Mayo Clinic's AI-ECG for weak heart pump (2019)
Cardiologist Paul Friedman's team at Mayo Clinic trained an AI on tens of thousands of 10-second ECGs to detect a weak heart pump (low left ventricular ejection fraction), a hallmark of heart failure. The model read signals invisible to the human eye and flagged patients with impaired pump function, published in Nature Medicine.
The study showed the concept worked, but it was a proof of principle, not a deployed tool.
It established that AI can extract hidden cardiac information from a standard ECG, laying the groundwork for the Imperial College tool.
This is the direct predecessor: the same condition (reduced pumping function), the same test (standard ECG), and the same approach (deep learning on ECG waveforms). The Imperial tool extends the concept to valve disease and scales it to millions of records.
MASAI trial: AI-assisted mammography (2023)
Swedish researchers randomized 80,000 women to AI-supported or standard mammography screening. The AI-assisted arm detected 20 percent more cancers (244 vs 203) with a lower recall rate, published in The Lancet Oncology. It was the first large randomized trial showing AI improves a standard screening test in real clinical practice.
The AI group caught more cancers without increasing false alarms, prompting calls for broader adoption.
It set the evidence bar for AI screening tools: randomized controlled trials, not retrospective validation.
The Imperial ECG tool has replicated the mammography AI's retrospective results. To reach routine use, it must clear the same hurdle MASAI did: prove real-world benefit in a prospective trial, which the 590-patient NHS study begins to address.
