3 ways machine learning will disrupt radiology--and the rest of medicine with it

Machine learning's expansive capacity to quickly turn big health data into evidence-based care will challenge all practitioners of medicine to either grow along with the technology or accept getting left behind by it. And radiologists will be among the first to feel its push (if they're not among the rads who are already working with it). So predict a pair of medical thought leaders in commentary published online Sept. 29 in the New England Journal of Medicine. Emergency physician Ziad Obermeyer, MD, MPhil, of Harvard and oncologist/bioethicist Ezekiel Emanuel, MD, PhD, of the University of Pennsylvania note that the AI subfield of machine learning draws out rules from data. This is distinct from AI "expert systems" algorithms, which apply human-created rules to draw conclusions about specific scenarios.