Computer scientists and pathologists at Stanford University now have a computer system that can look a tissue sample and diagnose breast cancer more accurately than a human doctor. The computer system, called C-Path (Computational Pathologist), even went one step further and identified previously-undiscovered cellular structures that can be used by computers and humans alike to improve the diagnosis and prognosis of breast cancer patients in the future.

Since 1928, tissue samples have been screened for breast cancer by hand. Pathologists examine the tumor under a microscope and, by measuring a handful of cellular features, can produce a fairly accurate diagnosis and prognosis for the patient. C-Path replaces the human looking down the microscope and uses computer vision to look for the same cancerous indicators. Furthermore — and this is what makes C-Path so accurate — by looking at a large number of human-diagnosed samples, the system learns. For example, one of the features that human doctors look for is the speed at which tumor cells divide by mitosis — through learning, C-Path might’ve discovered that mitosis isn’t actually the most accurate indicator.

Learning also allowed C-Path to discover new, cancer-related cellular factors — 6,642 in total — which it then used to diagnose and prognose new cancer patients with better accuracy than a human doctor. One of these indicators, related to the stroma (connective tissue between cells), was a completely new discovery — in other words, C-Path’s automated learning process just saved the lives of innumerable breast cancer patients around the world.

Beyond the immediate implications — hospitals around the world should get their hands on C-Path ASAP — the Stanford researchers are excited about what this means for the analysis and understanding of cancer in general. “Through machine learning, we are coming to think of cancer more holistically, as a complex system rather than as a bunch of bad cells in a tumor,” says Matt van de Rijn, co-author of the paper. There’s also the tantalizing possibility that C-Path might be able to predict the efficacy of different cancer treatments — a simulation task that computers would excel at.

Finally, C-Path could bring advanced medicine to the home — or to developing and third world nations. Cancer is even more of a killer in countries without Western healthcare, but imagine if you had a C-Path machine in every town or city. The coup de grace, though, is that C-Path could even be trained to detect pre-cancerous cells, possibly helping men and women avoid cancer in the first place.

Read more at Stanford or read the paper