Machine Learning can Help Doctors Diagnose Disease

Dr. Partho Sengupta had a hunch. A leading cardiologist now practicing at the West Virginia University Heart and Vascular Institute, Sengupta wanted to know whether the emerging field of machine learning could help solve a problem that had long vexed heart doctors. Driven by his conviction and curiosity, Sengupta cold-called data scientists at Saffron, a pioneering artificial intelligence company in North Carolina's Research Triangle acquired by Intel in 20151, with an idea for a novel experiment. Several phone calls and one proof of concept later, Sengupta and Saffron were able to show that a particular type of machine learning can be a powerful--even lifesaving--aid to cardiologists. The groundbreaking work also holds promise for delivering on the triple aims of healthcare reform: lowering costs, elevating quality of care, and improving access. The idea for the experiment had its genesis in Sengupta's office, where, like every other cardiologist, Sengupta struggled to diagnose between two very different diseases with dangerously similar symptoms.