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Can An Algorithm Diagnose Heart Disease Better Than A Person?

Yesterday, researchers at Stanford University introduced the latest thing in AI diagnostics: An algorithm that can sift through hours of heart rhythm data gathered by wearable monitors to determine whether a patient has an irregular heartbeat, or arrhythmia. The algorithm, the researchers say, is not only as good as a cardiologist at correctly diagnosing a condition, but often better.

Humans have been envisioning a future where machines replace doctors in the diagnosing process since the 1950s, when clinical psychologist Paul Meehl put forth the controversial idea in a book with a very boring sounding name. In Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence, he argued that simple, data-driven algorithms could make better decisions about patient diagnosis and treatment than trained clinical psychologists.

That claim went on to be replicated many times over across medicine — algorithms could, in another case, better predict cancer than radiologists. Recently, artificial intelligence and deep learning have upped the ante, promising algorithms that can not only make data-based healthcare decisions free from human error, but also process sets of data far more vast than any one human being ever could. Already on the market are deep-learning systems that assist in interpreting breast and heart imaging. Relying on image recognition, Google recently used AI to diagnose cancer faster than a human, and is testing it to diagnose diabetic blindnessThe new study suggests AI might be poised to overtake doctors in yet another critical area of diagnosis — spotting irregular heartbeats that could be life-threatening.


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