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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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. |
format | Online Article Text |
id | pubmed-10685136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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