Fewer False-positive Mammograms May Need Human-Computer Mix

Fewer False-positive Mammograms May Need Human-Computer Mix
Artificial intelligence (AI) could be combined with radiologist assessments to improve the accuracy of breast cancer screenings, a study suggests. The study, "Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms," was published in JAMA Network Open. Mammograms are commonly used to screen for breast cancer, and their images interpreted by radiologists. This brings is an element of human error  that may contribute to the relatively high false-positive rate in diagnoses: about 1 in 10 people who undergo a mammogram are called back for additional testing. On average, about 1 in 20 of those called back will have breast cancer. Some have suggested that using AI (that is, complex computer algorithms) to analyze mammography images could improve diagnostic accuracy by removing elements of human fallibility. The Dialogue on Reverse Engineering Assessment and Methods (DREAM) initiative has run many competitions aimed at generating computer-based methods to improve healthcare for various diseases. In this study, researchers reported on findings from the digital mammography DREAM challenge, which aimed to generate algorithms to improve the accuracy of mammography-based breast cancer screening. The challenge was co-organized by IBM, Sage Bionetworks, Kaiser Permanente Washington, and others, with funding from the Arnold Foundation. Overall, the challenge included 1,100 individuals, comprising 126 teams from 44 countries. Competitors submitted algorithms to be tested on two datasets, together containing data on more than 300,000 mammogram examinations done on over 150,000 individuals. For patient confidentiality purposes, data were maintained behind a firewall; competitors did not have direct access to the
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