Pathways to breast cancer screening arti ficial intelligence algorithm validation

被引:15
作者
Lee, Christoph I. [1 ]
Houssami, Nehmat [2 ]
Elmore, Joann G. [3 ]
Buist, Diana S. M. [4 ]
机构
[1] Univ Washington, Sch Med, Dept Radiol, Dept Hlth Serv,Sch Publ Hlth,Hutchinson Inst Canc, Seattle, WA 98195 USA
[2] Univ Sydney, Sydney Sch Publ Hlth, Fac Med & Hlth, Sydney, NSW, Australia
[3] Univ Calif Los Angeles, David Geffen Sch Med, Dept Med, Los Angeles, CA 90095 USA
[4] Kaiser Permanente Washington Hlth Res Inst, Seattle, WA USA
关键词
Arti ficial intelligence; Breast cancer; Screening; Mammography; Population health; Validation; Transparency; Reproducibility; COMPUTER-AIDED DETECTION; MAMMOGRAPHY; TOMOSYNTHESIS;
D O I
10.1016/j.breast.2019.09.005
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
As 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. (c) 2019 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:146 / 149
页数:4
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