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Methods: follow PRISMA, search PubMed, EMBASE, and Cochrane with “Artificial Intelligence” and “Mammography” terms and corresponding MeSH; include English publications from 2014–2024. Results: 1,878 records found, 30 studies included after deduplication and PICO screening; AUC and sensitivity align with independent AI performance relative to radiologists, and combining AI with radiologists improves accuracy. Conclusion: AI supports lesion detection and enhances radiologists’ performance.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/artificial-intelligence-in-mammography-a-systematic-review-of-the-external-validation/332576/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/artificial-intelligence-in-mammography-a-systematic-review-of-the-external-validation/332576.png","ImageObject",300,407,{"name":92,"@type":93},"Riley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main objective of the systematic review?","Question",{"text":112,"@type":113},"To conduct a systematic review of external validation studies on different artificial intelligence algorithms used for breast cancer screening with mammography.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which databases and time period were searched?",{"text":117,"@type":113},"The review searched PubMed, EMBASE, and Cochrane and filtered English publications from the past ten years (2014–2024) using terms related to “Artificial Intelligence” and “Mammography” plus corresponding MeSH terms.",{"name":119,"@type":110,"acceptedAnswer":120},"How many studies were ultimately included?",{"text":121,"@type":113},"A total of 1,878 articles were identified; after removing 373 duplicates and excluding 1,475 that did not address the PICO question, 30 studies were included.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},332576,1790102460,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},1374391975076,"https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051","Review article  \nArtificial intelligence in mammography: a systematic review of the external validation  \nPaulo Eduardo Souza Castelo Branco1  \n [https://orcid.org/0000-0001-8077-1187](https://orcid.org/0000-0001-8077-1187)  \nAdriane Helena Silva Franco1  \n [https://orcid.org/0000-0003-0497-872X](https://orcid.org/0000-0003-0497-872X)  \nAmanda Prates de Oliveira1  \n [https://orcid.org/0009-0006-1094-9342](https://orcid.org/0009-0006-1094-9342)  \nIsabela Maurício Costa Carneiro1  \n [https://orcid.org/0009-0006-6720-1503](https://orcid.org/0009-0006-6720-1503)  \nLuciana Maurício Costa de Carvalho1  \n [https://orcid.org/0000-0002-1904-9942](https://orcid.org/0000-0002-1904-9942)  \nJonathan Igor Nunes de Souza2  \n [https://orcid.org/0000-0002-0401-4843](https://orcid.org/0000-0002-0401-4843)  \nDanniel Rodrigo Leandro3  \n [https://orcid.org/0009-0001-1852-9303](https://orcid.org/0009-0001-1852-9303)  \nEduardo Batista Cândido3  \n [https://orcid.org/0000-0001-6496-6654](https://orcid.org/0000-0001-6496-6654)  \n1Faculdade de Medicina, Faculdade de Minas, Belo Horizonte, MG, Brazil.  \n2Faculdade de Medicina, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, MG, Brazil.  \n3Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.  \nConflicts to interest: none to declare.  \nHow to cite  \nBranco PE, Franco AH, Oliveira AP, Carneiro IM, Carvalho LM, Souza JI, et al. Artificial intelligence in mammography: a systematic review of the external validation. Rev Bras Ginecol Obstet. 2024;46:e-rbgo71.  \nDOI  \n[http://dx.doi.org/10.61622/rbgo/2024rbgo71](http://dx.doi.org/10.61622/rbgo/2024rbgo71)  \nKeywords  \nArtificial Intelligence; Mammography; Deep learning; Breast neoplasms; Machine learning  \nSubmitted  \nNovember 1, 2023  \nAccepted  \nMay 27, 2024  \nCorresponding author  \nPaulo Castelo Branco  \nE-mail: castelobrancomd@gmail.com  \nAssociate Editor  \nCassio Cardoso Filho  \n([https://orcid.org/0000-0002-1895-0106](https://orcid.org/0000-0002-1895-0106)) Universidade Estadual de Campinas, Campinas, SP, Brasil  \nAbstract  \nObjective: To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography.  \nData source: Our systematic review was conducted and reported following the PRISMA statement, using the PubMed, EMBASE, and Cochrane databases with the search terms “Artificial Intelligence,”“Mammography,” and their respective MeSH terms. We filtered publications from the past ten years (2014 – 2024) and in English.  \nStudy selection: A total of 1,878 articles were found in the databases used in the research. After removing duplicates (373) and excluding those that did not address our PICO question (1,475), 30 studies were included in this work.  \nData collection: The data from the studies were collected independently by five authors, and it was subsequently synthesized based on sample data, location, year, and their main results in terms of AUC, sensitivity, and specificity.  \nData synthesis: It was demonstrated that the Area Under the ROC Curve (AUC) and sensitivity were similar to those of radiologists when using independent Artificial Intelligence. When used in conjunction with radiologists, statistically higher accuracy in mammogram evaluation was reported compared to the assessment by radiologists alone.  \nConclusion: AI algorithms have emerged as a means to complement and enhance the performance and accuracy of radiologists. They also assist less experienced professionals in detecting possible lesions. Furthermore, this tool can be used to complement and improve the analyses conducted by medical professionals.  \nRev Bras Ginecol Obstet. 2024;46:e-rbgo71.  1   \nArtificial intelligence in mammography: a systematic review of the external validation  \nBranco PE, Franco AH, Oliveira AP, Carneiro IM, Carvalho LM, Souza JI, et al  \nIntroduction  \nBreast cancer is the most common neoplasm in women worldwide. In Brazil, this sc","cbCairlYjvD5sfXL","https://ap.wps.com/l/cbCairlYjvD5sfXL","pdf",189191,"English","# Abstract\n## Objective\n## Data source\n## Study selection\n## Data collection\n## Data synthesis\n## Conclusion\n# Introduction\n## Breast cancer burden and screening context\n## Mammography and BI-RADS\n## Limitations and role of AI in screening","[{\"question\":\"What is the main objective of the systematic review?\",\"answer\":\"To conduct a systematic review of external validation studies on different artificial intelligence algorithms used for breast cancer screening with mammography.\"},{\"question\":\"Which databases and time period were searched?\",\"answer\":\"The review searched PubMed, EMBASE, and Cochrane and filtered English publications from the past ten years (2014–2024) using terms related to “Artificial Intelligence” and “Mammography” plus corresponding MeSH terms.\"},{\"question\":\"How many studies were ultimately included?\",\"answer\":\"A total of 1,878 articles were identified; after removing 373 duplicates and excluding 1,475 that did not address the PICO question, 30 studies were included.\"}]","Artificial intelligence in mammography - a systematic review of the external validation | PDF",1789998785,18]