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This study assesses whether breast radiologists’ diagnostic performance changes when supported by AI-based computer-aided detection (AI-CAD) in South Korea’s national screening program. Among 24,543 women, screen-detected breast cancer detection rate increased by 13.8% with AI-CAD, while recall rates showed no significant difference, indicating improved detection without added recalls.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/artificial-intelligence-for-breast-cancer-screening-in-mammography-ai-stream-preliminary-analysis-of-a-prospective-multicenter-cohort-study/369264/",{"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-for-breast-cancer-screening-in-mammography-ai-stream-preliminary-analysis-of-a-prospective-multicenter-cohort-study/369264.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-23",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 was the study’s main objective?","Question",{"text":112,"@type":113},"To compare breast radiologists’ diagnostic accuracy when screening mammograms with and without AI-based computer-aided detection (AI-CAD) in a real-world single-read setting.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the study conducted and who were included?",{"text":117,"@type":113},"A prospective multicenter cohort study was conducted within South Korea’s national breast cancer screening program, including 24,543 women in the final cohort.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the key findings regarding cancer detection and recall rates?",{"text":121,"@type":113},"Cancer detection rate (CDR) was significantly higher with AI-CAD (13.8% increase) while recall rates (RRs) did not differ significantly.","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},369264,1790616486,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"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":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Article [https://doi.org/10.1038/s41467-025-57469-3](https://doi.org/10.1038/s41467-025-57469-3)  \nArtificial intelligence for breast cancer screening in mammography (AI-STREAM):  \npreliminary analysis of a prospective multicenter cohort study  \nReceived: 26 June 2024  \n\n| Accepted: 22 January 2025 |\n| --- |\n| |\n| Check for updates |\n\nYun-Woo Chang 1 , Jung Kyu Ryu2, Jin Kyung An3, Nami Choi4, Young Mi Park5, Kyung Hee Ko6,7 & Kyunghwa Han 8  \nArtificial intelligence (AI) improves the accuracy of mammography screening, but prospective evidence, particularly in a single-read setting, remains limited. This study compares the diagnostic accuracy of breast radiologists with and without AI-based computer-aided detection (AI-CAD) for screening mammograms in a real-world, single-read setting. A prospective multicenter cohort study is conducted within South Korea’s national breast cancer screening program for women. The primary outcomes are screen-detected breast cancer within one year, with a focus on cancer detection rates (CDRs) and recall rates (RRs) of radiologists. A total of 24,543 women are included in the final cohort, with 140 (0.57%) screen-detected breast cancers. The CDR is significantly higher by 13.8% for breast radiologists using AI-CAD (n =140 [5.70‰]) compared to those without AI (n =123 [5.01‰]; p \u003C0.001), with no significant difference in RRs (p = 0.564). These preliminary results show a significant improvement in CDRs without affecting RRs in a radiologist’s standard singlereading setting ([ClinicalTrials.gov: NCT05024591](ClinicalTrials.gov: NCT05024591)).  \nWorldwide, breast cancer is the most commonly diagnosed cancer in women and the leading cause of mortality among them. Randomized controlled trials, systemic reviews, and observational studies have demonstrated that including mammography in breast cancer screening can reduce breast cancer-related mortality rates by approximately 20–50%1–3. Accordingly, several countries have currently implemented screening programs that incorporate mammography for early detection and treatment of breast cancer, aiming to reduce mortality and morbidity. While mammography screening has proven effective in detecting early cancer, it can lead to undesirable  \nfalse-positive recalls that may require additional imaging evaluation or biopsies. While early detection is the key to screening, breast cancer diagnosis can be alternatively delayed for inaccurate false negative interpretation of mammography; failure to recognize due to low sensitivity in dense breasts, misinterpretation due to lack of recognition of abnormal features, or a wrong interpretation due to no change compared to the previous study, previous biopsy site, or benign appearing lesion characteristics4–6. Roughly 20–33% of breast cancer cases are diagnosed based on symptoms between consecutive screening rounds (ie, interval cancers) or discovered  \n1Department of Radiology, Soonchunhyang University Seoul Hospital, Seoul, Korea. 2Department of Radiology, Kyung Hee University Hospital at Gangdong, Seoul, Korea. 3Department of Radiology, Nowon Eulgi University Hospital, Seoul, Korea. 4Department of Radiology, Konkuk University Medical center, Seoul, Korea. 5Department of Radiology, Inje University Busan Paik Hospital, Busan, Korea. 6Department of Radiology, CHA Bundang Medical center, Seongnam, Korea. 7Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea. 8Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea. e-mail: [ywchang@schmc.ac.kr](ywchang@schmc.ac.kr)  \nthrough other imaging modalities5. To overcome these shortcomings, significant efforts and improvements have been made by performing double readings with two radiologists, increasing the frequency of screening examinations, or implementing additional supplementary imaging modalities4,7. Mul","cbCaif4H0u1iyc27","https://ap.wps.com/l/cbCaif4H0u1iyc27","pdf",888583,11,"English","# Study design and objectives\n## Outcomes and cohort results\n# Background and screening challenges\n## False positives and interval cancers\n## Prior work and knowledge gaps\n# Evaluation approach in single-reading strategy","[{\"question\":\"What was the study’s main objective?\",\"answer\":\"To compare breast radiologists’ diagnostic accuracy when screening mammograms with and without AI-based computer-aided detection (AI-CAD) in a real-world single-read setting.\"},{\"question\":\"How was the study conducted and who were included?\",\"answer\":\"A prospective multicenter cohort study was conducted within South Korea’s national breast cancer screening program, including 24,543 women in the final cohort.\"},{\"question\":\"What were the key findings regarding cancer detection and recall rates?\",\"answer\":\"Cancer detection rate (CDR) was significantly higher with AI-CAD (13.8% increase) while recall rates (RRs) did not differ significantly.\"}]","Artificial intelligence for breast cancer screening in mammography (AI-STREAM) - preliminary analysis of a prospective multicenter cohort study | PDF",1790181651,28]