|By PR Newswire||
|November 30, 2016 01:45 PM EST|
SANTA MONICA, Calif., Nov. 30, 2016 /PRNewswire/ -- DeepRadiology, a deep learning artificial intelligence medical startup is proud to announce the world's first fully autonomous system for medical image interpretation. The system is able to produce final interpretations and reporting on medical imaging studies without the need for a radiologist.
The unique software system was created using the latest artificial intelligence techniques, the knowledge contained in every major radiology textbook on the subject, and the cumulative experience of reviewing many millions of medical images. This turns out to numerically represent several human radiologists' lifetimes of experience. The device is not yet available for marketing in the United States and is currently being evaluated by the U.S. Food and Drug Administration.
In addition to experts in radiology and artificial intelligence we are fortunate to have Yann LeCun, widely regarded as the 'father of deep learning' as a key part of our team. "DeepRadiology has an amazing technical team of world leaders in AI and medical imaging. It is uniquely poised to transform healthcare," says Professor Fei Fei Li, Director of the Stanford Artificial Intelligence Laboratory.
DeepRadiology will be exhibiting at the annual meeting of the Radiological Society of North America (RSNA) which is the largest radiology meeting in the world with over 50,000 attendees. The RSNA will be meeting in Chicago from November 27 through December 1, 2016. Please visit us at McCormick Place, 2301 S King Dr, North Hall, Booth 8503.
DeepRadiology is a medical deep learning artificial intelligence company bringing together the brightest minds in the field to create revolutionary products that transform healthcare. DeepRadiology is headquartered in Southern California. For more information, visit http://www.deepradiology.com.
To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/deepradiology-announces-the-worlds-first-fully-autonomous-radiology-interpretation-system-300370689.html
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