Invasive Breast Cancers Missed by AI Screening of Mammograms: Clinical Implications and Diagnostic Challenges
Invasive Breast Cancers Missed by AI Screening of Mammograms: Clinical Implications and Diagnostic Challenges Cánceres de mama invasivos no detectados mediante mamografías con detección artificial: implicaciones clínicas y desafíos diagnósticos AI 기반 유방촬영술 검진에서 놓친 침습성 유방암: 임상적 의미와 진단 과제 doi: 10.1148/radiol.242408 Introduction Mammography remains the gold standard in breast cancer screening, credited with reducing breast cancer mortality by up to 50%. However, its sensitivity ranges between 75% and 85%, and drops further to 30% to 50% in women with dense breast tissue. To address these limitations, artificial intelligence (AI)-based systems have been introduced, with some outperforming radiologists in diagnostic accuracy. Despite these advances, AI algorithms are not infallible and may fail to detect certain types of breast cancers. This column provides an in-depth analysis of the false-negative rate (FNR) and the imaging features of invasive breast cancers missed by AI, based on ...