Artificial intelligence for breast cancer screening: Opportunity or hype?

Houssami, N; Lee, CI; Buist, DSM; Tao, D

Artificial intelligence for breast cancer screening: Opportunity or hype?

Houssami, N Lee, CI Buist, DSM Tao, D

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Publication Type:: Journal Article
Citation:: Breast, 2017, 36 pp. 31 - 33
Issue Date:: 2017-12-01

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Field	Value	Language
dc.contributor.author	Houssami, N	en_US
dc.contributor.author	Lee, CI	en_US
dc.contributor.author	Buist, DSM	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.available	2017-09-12	en_US
dc.date.issued	2017-12-01	en_US
dc.identifier.citation	Breast, 2017, 36 pp. 31 - 33	en_US
dc.identifier.issn	0960-9776	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123591
dc.description.abstract	© 2017 Elsevier Ltd 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.	en_US
dc.relation.ispartof	Breast	en_US
dc.relation.isbasedon	10.1016/j.breast.2017.09.003	en_US
dc.subject.classification	Oncology & Carcinogenesis	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Breast Neoplasms	en_US
dc.subject.mesh	Image Interpretation, Computer-Assisted	en_US
dc.subject.mesh	Mammography	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Early Detection of Cancer	en_US
dc.title	Artificial intelligence for breast cancer screening: Opportunity or hype?	en_US
dc.type	Journal Article
utslib.citation.volume	36	en_US
utslib.for	1103 Clinical Sciences	en_US
utslib.for	1117 Public Health and Health Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

© 2017 Elsevier Ltd 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/123591