{"id":2325,"date":"2025-09-15T16:04:48","date_gmt":"2025-09-15T16:04:48","guid":{"rendered":"https:\/\/www.lunit.io\/en\/?post_type=publication&#038;p=2325"},"modified":"2025-11-06T01:28:27","modified_gmt":"2025-11-06T01:28:27","slug":"clinical-application-of-ai-in-mammography-insights-from-a-prospective-study","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/en\/publication\/clinical-application-of-ai-in-mammography-insights-from-a-prospective-study\/","title":{"rendered":"Clinical Application of AI in Mammography: Insights from a Prospective Study"},"content":{"rendered":"<h3 id=\"screen-reader-main-title\" class=\"Head u-font-serif u-h2 u-margin-s-ver\"><span class=\"title-text\">Clinical Application of AI in Mammography: Insights from a Prospective Study<\/span><\/h3>\n<p>Ebru Yilmaz, Mustafa Ege Seker, Nilgun Guldogan, Ebru Banu Turk, Servet Erdemli, Yilmaz Onat Koyluoglu, Sehla Nurefsan Sancak, Erkin Aribal<\/p>\n<p><strong>Academic Radiology, 2025<\/strong><\/p>\n<p><strong>Abstract<\/strong><\/p>\n<p><strong>Rationale and Objectives<\/strong><\/p>\n<p>This prospective study evaluated the performance of AI in a diagnostic clinic setting, comparing its effectiveness with radiologists of varying experience.<\/p>\n<p><strong>Materials and Methods<\/strong><\/p>\n<p>The study was conducted at a single center and included 1063 patients undergoing diagnostic or screening mammography. Five radiologists with different experience levels assessed the images using the fifth edition of the BI-RADS lexicon. Standalone AI software assigned risk scores (0\u2212100), with scores above 30.44 considered positive. AI risk assessments were compared with radiologists\u2019 BI-RADS scores. Radiologists also re-evaluated AI-positive mammograms as a second look. Ground truth was established through histopathology and two years of follow-up.<\/p>\n<p><strong>Results<\/strong><\/p>\n<p>Right and left breasts were analyzed separately, and 2126 mammography images were evaluated from 1063 women. A total of 29 cancers were diagnosed in 28 women. Among all examinations, 2.44% (52\/2126) were positive, of which 46.15% (24\/52) were true positive. Standalone AI detected 82.75% (24\/29) of cancers, and the majority voting of radiologists scored positive (BI-RADS 0,4 and 5) in 8% (172\/2126) where 89.65% (26\/29) of cancers were detected. The AUC score of majority voting was 94.7% (95% CI: 91.1\u201398.3), and AI was 94.4% (95% CI: 88.5\u2013100). AI was statistically not significantly different than (p=0.79) AUC of the majority voting. The re-evaluation assessment of AI-flagged images achieved an AUC of 94.8% (95% CI: 91.2\u201398.3), significantly different from the initial evaluation (p=0.015). However, it was not significantly different from AI (p=0.74).<\/p>\n<p><strong>Conclusion<\/strong><\/p>\n<p>AI algorithms in diagnostic settings can serve as effective CAD systems, aiding in breast cancer detection and reducing inter-reader variability.<\/p>\n<p style=\"text-align: center;\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1076633225004532\"><strong>Read the full paper<\/strong><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[],"publication-region":[89],"publication-type":[],"radiology":[103,97,101,96],"class_list":["post-2325","publication","type-publication","status-publish","hentry","publication-region-europe","radiology-ai-generalization-real-world-evidence","radiology-breast","radiology-diagnostic-symptomatic-settings","radiology-lunit-insight"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Clinical Application of AI in Mammography: Insights from a Prospective Study - 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