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Review: Predictive approaches to breast cancer risk

Despite the deployment of specific breast cancer screening strategies, breast cancer incidence rates have escalated significantly over recent decades. In a bid to reverse this trend, scientists have engaged in extensive epidemiological research into breast cancer prevalence, identifying numerous ind...

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Detalles Bibliográficos
Autores principales: Huang, Shuai, Xu, Jun Tao, Yang, Mei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685136/
https://www.ncbi.nlm.nih.gov/pubmed/38034632
http://dx.doi.org/10.1016/j.heliyon.2023.e21344
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author Huang, Shuai
Xu, Jun Tao
Yang, Mei
author_facet Huang, Shuai
Xu, Jun Tao
Yang, Mei
author_sort Huang, Shuai
collection PubMed
description Despite the deployment of specific breast cancer screening strategies, breast cancer incidence rates have escalated significantly over recent decades. In a bid to reverse this trend, scientists have engaged in extensive epidemiological research into breast cancer prevalence, identifying numerous individual risk factors and promoting population-wide health education. Coupled with advances in genetic testing, risk prediction models based on breast cancer genes have been developed, albeit with inherent limitations. In the new millennium, the emergence of artificial intelligence (AI) as a dominant technological force suggests that breast cancer prediction models developed with AI may represent the next frontier in research.
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spelling pubmed-106851362023-11-30 Review: Predictive approaches to breast cancer risk Huang, Shuai Xu, Jun Tao Yang, Mei Heliyon Review Article Despite the deployment of specific breast cancer screening strategies, breast cancer incidence rates have escalated significantly over recent decades. In a bid to reverse this trend, scientists have engaged in extensive epidemiological research into breast cancer prevalence, identifying numerous individual risk factors and promoting population-wide health education. Coupled with advances in genetic testing, risk prediction models based on breast cancer genes have been developed, albeit with inherent limitations. In the new millennium, the emergence of artificial intelligence (AI) as a dominant technological force suggests that breast cancer prediction models developed with AI may represent the next frontier in research. Elsevier 2023-10-31 /pmc/articles/PMC10685136/ /pubmed/38034632 http://dx.doi.org/10.1016/j.heliyon.2023.e21344 Text en © 2023 The Authors https://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 Review Article
Huang, Shuai
Xu, Jun Tao
Yang, Mei
Review: Predictive approaches to breast cancer risk
title Review: Predictive approaches to breast cancer risk
title_full Review: Predictive approaches to breast cancer risk
title_fullStr Review: Predictive approaches to breast cancer risk
title_full_unstemmed Review: Predictive approaches to breast cancer risk
title_short Review: Predictive approaches to breast cancer risk
title_sort review: predictive approaches to breast cancer risk
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685136/
https://www.ncbi.nlm.nih.gov/pubmed/38034632
http://dx.doi.org/10.1016/j.heliyon.2023.e21344
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