With AI, machines become expert at reading brain scans

A computer algorithm developed by scientists at the University of California, San Francisco (UCSF), and UC Berkeley bested two out of four expert radiologists at finding tiny brain hemorrhages in head scans -- an advance that one day may help doctors treat patients with traumatic brain injuries, strokes and aneurysms. Radiologists typically look at thousands of brain images each day, searching for tiny abnormalities that can signal life-threatening emergencies. A single, three-dimensional, computed tomography scan can produce a stack of 30 or more images, each of which must be reviewed by a radiologist. The researchers created their algorithm to see if artificial intelligence could more efficiently and accurately pick out images with significant abnormalities to help radiologists focus on the most important images and examine them more closely. "We wanted something that was practical, and for this technology to be useful clinically, the accuracy level needs to be close to perfect," said study co-author Esther Yuh, an associate professor of radiology at UCSF. "The performance bar is high for this application, due to the potential consequences of a missed abnormality, and people won't tolerate less than human performance or accuracy."