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Pathways to breast cancer screening artificial intelligence algorithm validation

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...

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Detalles Bibliográficos
Autores principales: Lee, Christoph I., Houssami, Nehmat, Elmore, Joann G., Buist, Diana S.M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7061055/
https://www.ncbi.nlm.nih.gov/pubmed/31540699
http://dx.doi.org/10.1016/j.breast.2019.09.005
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author Lee, Christoph I.
Houssami, Nehmat
Elmore, Joann G.
Buist, Diana S.M.
author_facet Lee, Christoph I.
Houssami, Nehmat
Elmore, Joann G.
Buist, Diana S.M.
author_sort Lee, Christoph I.
collection PubMed
description 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.
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spelling pubmed-70610552020-07-29 Pathways to breast cancer screening artificial intelligence algorithm validation Lee, Christoph I. Houssami, Nehmat Elmore, Joann G. Buist, Diana S.M. Breast Virtual special issue: Artificial Intelligence in Breast Cancer Care; Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi 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. Elsevier 2019-09-09 /pmc/articles/PMC7061055/ /pubmed/31540699 http://dx.doi.org/10.1016/j.breast.2019.09.005 Text en © 2019 Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Virtual special issue: Artificial Intelligence in Breast Cancer Care; Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi
Lee, Christoph I.
Houssami, Nehmat
Elmore, Joann G.
Buist, Diana S.M.
Pathways to breast cancer screening artificial intelligence algorithm validation
title Pathways to breast cancer screening artificial intelligence algorithm validation
title_full Pathways to breast cancer screening artificial intelligence algorithm validation
title_fullStr Pathways to breast cancer screening artificial intelligence algorithm validation
title_full_unstemmed Pathways to breast cancer screening artificial intelligence algorithm validation
title_short Pathways to breast cancer screening artificial intelligence algorithm validation
title_sort pathways to breast cancer screening artificial intelligence algorithm validation
topic Virtual special issue: Artificial Intelligence in Breast Cancer Care; Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7061055/
https://www.ncbi.nlm.nih.gov/pubmed/31540699
http://dx.doi.org/10.1016/j.breast.2019.09.005
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