If this sounds like science fiction, it’s not. It’s what health care might seem like to doctors, patients, and regulators around the world as new methods in machine learning offer more insights from ever-growing amounts of data.

Complex algorithms will soon help clinicians make incredibly accurate determinations about our health from large amounts of information, premised on largely unexplainable correlations in that data. This future is alarming, no doubt, due to the power that doctors and patients will start handing off to machines.

But it’s also a future that we must prepare for — and embrace — because of the impact these new methods will have and the lives we can potentially save. Take, for example, a study released today by a group of researchers from the University of Chicago, Stanford University, the University of California, San Francisco, and Google.

The study, which one of us coauthored, fed de-identified data on hundreds of thousands of patients into a series of machine learning algorithms powered by Google’s massive computing resources. With extraordinary accuracy, these algorithms were able to predict and diagnose diseases, from cardiovascular illnesses to cancer, and predict related things such as the likelihood of death, the length of hospital stay, and the chance of hospital readmission.

Within 24 hours of a patient’s hospitalization, for example, the algorithms were able to predict with over 90% accuracy the patient’s odds of dying. These predictions, however, were based on patterns in the data that the researchers could not fully explain. Read more from hbr.org…

thumbnail courtesy of hbr.org