Artificial intelligence for breast cancer screening: Opportunity or hype?. (December 2017)

• Record Type: Journal Article
• Authors: Houssami, Nehmat; Lee, Christoph I.; Buist, Diana S.M.; Tao, Dacheng
• Is Part Of: Breast. Volume 36(2017)
• Page Start: 31
• Page End: 33
• Publication Date: 2017-12
• Subjects: Artificial intelligence -- Mammography -- Population screening; Breast -- Diseases -- Periodicals; Breast -- Tumors -- Periodicals; Breast -- Periodicals; Electronic journals; Periodicals; 616
• DOI: 10.1016/j.breast.2017.09.003
• Languages: English
• ISSNs: 0960-9776

Abstract

Abstract: Interpretation of mammography for breast cancer (BC) screening can confer a mortality benefit through early BC detection, can miss a cancer that is present or fast growing, or can result in false-positives. Efforts to improve screening outcomes have mostly focused on intensifying imaging practices (double instead of single-reading, more frequent screens, or supplemental imaging) that may add substantial resource expenditures and harms associated with population screening. Less attention has been given to making mammography screening practice 'smarter' or more efficient. Artificial intelligence (AI) is capable of advanced learning using large complex datasets and has the potential to perform tasks such as image interpretation. With both highly-specific capabilities, and also possible un-intended (and poorly understood) consequences, this viewpoint considers the promise and current reality of AI in BC detection.