Whitney Palmer

In Radiology, Man Versus Machine

Published on the Feb. 11, 2016 DiagnosticImaging.com website

By Whitney L.J. Howell

Call it artificial intelligence. Deep learning. Computer cognition. Whatever its name, it’s the same thing – machines recognizing clinical problems in digital images ahead of the radiologists charged with making the diagnosis.

The artificial intelligence (AI) trend is new, but it’s gaining ground quickly, according to industry experts. The advent of these technologies and radiology’s growing interest in and dependence on them has been discussed at national and international meetings, including the RSNA, HIMSS, and SIIM annual meetings, during the past year. But, there’s still a long way to go.

“We’re just barely scratching the surface of using artificial intelligence in the last few years,” said Eliot Siegel, MD, professor and vice chair of research information systems for the University of Maryland Department of Diagnostic Radiology and Nuclear Medicine. “There’s an emergence of increasing interest in the largest companies in the world, including Google, Microsoft, Apple, and IBM, in actually starting to use these technologies for data extraction and evaluation.”

AI opens the door for radiologists to compare new images with similar, existing ones, said Siegel who also serves as the chief of imaging for the VA Maryland Healthcare System and has spoken about AI use in radiology.

To read the remainder of the article at its original location: http://www.diagnosticimaging.com/pacs-and-informatics/radiology-man-versus-machine

February 11, 2016 - Posted by wjpalmer | Healthcare, Science | benefits artificial intelligence radiology, challenges of artificial intelligence radiology, Eliot Siegel MD and radiology artificial intelligence, Enlitic, Igor Barani MD, Jenny Chen MD, Merge Healthcare, radiology and artificial intelligence, radiology and machine learning, Steve Tolle