December 19, 2019

At Mayo Clinic, AI engineers face an ‘acid test’: Will their algorithms help real patients?

By Karl W Oestreich

STAT
by Casey Ross

It would be easy to wonder what Zachi Attia is doing in the cardiac operating rooms of one of America’s most prestigious hospitals. He has no formal medical training or surgical expertise. He cannot treat arrhythmias, repair heart valves, or unclog arteries. The first time he watched a live procedure, he worried he might faint. But at Mayo Clinic, the 33-year-old machine learning engineer has become a central figure in one of the nation’s most ambitious efforts to revamp heart disease treatment using artificial intelligence. Working side by side with physicians, he has built algorithms that in studies have shown a remarkable ability to unmask heart abnormalities long before patients begin experiencing symptoms. Much of that work involves training computers to sift through mountains of patient data to pluck out patterns invisible to even the most skilled cardiologists.

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Additional coverage: Kaiser Health News

Context: A Mayo Clinic research study shows that artificial intelligence (AI) can detect the signs of an irregular heart rhythm — atrial fibrillation (AF) — in an EKG, even if the heart is in normal rhythm at the time of a test. In other words, the AI-enabled EKG can detect recent atrial fibrillation that occurred without symptoms or that is impending, potentially improving treatment options. This research could improve the efficiency of the EKG, a noninvasive and widely available method of heart disease screening. The findings and an accompanying commentary are published in The Lancet. You can read more about the research on Mayo Clinic News Network.

Contact: Traci Klein

Tags: artificial Intelligence, Dr. David Rushlow, Dr. Eric Topol, Dr. Paul Friedman, Dr. Peter Noseworthy, Dr. Zachi Attia, heart disease treatment, machine learning, STAT, Uncategorized

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