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Breast. 2017 Dec;36:31-33. doi: 10.1016/j.breast.2017.09.003. Epub 2017 Sep 20.

Artificial intelligence for breast cancer screening: Opportunity or hype?

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Sydney School of Public Health, Sydney Medical School, University of Sydney, Australia. Electronic address:
Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
UBTECH Sydney AI Centre, School of Information Technologies, University of Sydney, Australia.


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.


Artificial intelligence; Mammography; Population screening

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