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dc.contributor.authorLee, Christoph I
dc.contributor.authorHoussami, Nehmat
dc.contributor.authorElmore, Joann G
dc.contributor.authorBuist, Diana S.M
dc.date.accessioned2021-07-29T06:18:05Z
dc.date.available2021-07-29T06:18:05Z
dc.date.issued2020en_AU
dc.identifier.urihttps://hdl.handle.net/2123/25794
dc.description.abstractAs more artificial intelligence (AI)-enhanced mammography screening tools enter the clinical market, greater focus will be placed on external validation in diverse patient populations. In this viewpoint, we outline lessons learned from prior efforts in this field, the need to validate algorithms on newer screening technologies and diverse patient populations, and conclude by discussing the need for a framework for continuous monitoring and recalibration of these AI tools. Sufficient validation and continuous monitoring of emerging AI tools for breast cancer screening will require greater stakeholder engagement and the creation of shared policies and guidelines.en_AU
dc.language.isoenen_AU
dc.publisherElsevieren_AU
dc.relation.ispartofThe Breasten_AU
dc.rightsCopyright All Rights Reserveden_AU
dc.subjectArtificial intelligenceen_AU
dc.subjectBreast canceren_AU
dc.subjectScreeningen_AU
dc.subjectMammographyen_AU
dc.subjectPopulation healthen_AU
dc.subjectValidationen_AU
dc.subjectTransparencyen_AU
dc.subjectReproducibilityen_AU
dc.titlePathways to breast cancer screening artificial intelligence algorithm validationen_AU
dc.typeArticleen_AU
dc.subject.asrc1112 Oncology and Carcinogenesisen_AU
dc.subject.asrc1117 Public Health and Health Servicesen_AU
dc.identifier.doi10.1016/j.breast.2019.09.005
dc.relation.otherBreast Cancer Institute P01 CA154292
dc.relation.otherAmerican Cancer Society MRSG-14-160-01-CPHPS
dc.relation.otherNational Cancer Institute R37 CA240403
dc.relation.otherNational Cancer Institute (R01 CA225585; U01 CA231782; R01 CA200690; R01 CA201376)
dc.relation.otherPatient Centered Outcomes Research Institute (PCS-1504-30370)
dc.relation.otherNational Cancer Institute National Cancer Institute (R01CA207375, R01CA222090, UM1 CA221940, R01 CA240375)
dc.relation.otherAgency for Health Care and Quality (K12 HS026369)
usyd.facultySeS faculties schools::Faculty of Medicine and Health::Sydney School of Public Healthen_AU
usyd.citation.volume52en_AU
usyd.citation.spage146en_AU
usyd.citation.epage149en_AU
workflow.metadata.onlyNoen_AU


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