Combining AI with Assessments from Radiologists Could Help Improve

In a study published today in the journal JAMA Network Open, researchers demonstrated that machine-learning algorithms could help improve the accuracy of breast cancer screenings when used in combination with assessments from radiologists. The study was based on results from the Digital Mammography (DM) DREAM Challenge, a crowd-sourced competition that kicked off in 2016 to engage a broad, international scientific community to assess whether artificial intelligence (AI) algorithms could meet or beat radiologist interpretive accuracy. New study in @JAMANetworkOpen shows #AI may help improve the accuracy of breast cancer screenings when used in combination with assessments from radiologists. "This DREAM Challenge allowed for a rigorous, apples-to-apples assessment of dozens of state-of-the-art deep learning algorithms in two independent datasets," said Dr. Justin Guinney, VP of Computational Oncology at Sage Bionetworks and Chair of DREAM Challenges. "This is a much-needed comparison effort given the importance and activity of AI research in this field."