Published in Lancet Digital Health, the new tool, called ECG-CLIP, was trained using more than 1.7 million ECGs collected from more than 540,000 people and paired with clinicians’ notes, embedding the tool with knowledge that may make it more adaptable for different disease detection and prediction tasks in real-world clinical environments.

“Our new algorithm only needs to see on the order of a dozen confirmed ECGs of a specific disease to detect that disease in the future,” says senior author Giorgio Quer, an assistant professor of digital medicine at Scripps Research.