Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review

作     者：Dave, Daksh Akhunzada, Adnan Ivkovic, Nikola Gyawali, Sujan Cengiz, Korhan Ahmed, Adeel Al-Shamayleh, Ahmad Sami 

作者机构：Birla Inst Technol & Sci Dept Elect Elect Pilani India Univ Doha Sci Technol Coll Comp IT Doha Qatar Univ Zagreb Fac Org & Informat Pavlinska Varazdin Croatia Lamar Univ Dept Comp Sci Beaumont TX USA Biruni Univ Dept Elect Elect Engn Istanbul Turkiye Univ Haripur Dept Informat Technol Haripur Pakistan Al Ahliyya Amman Univ Fac Informat Technol Dept Networks & Cybersecur Amman Jordan 

出 版 物：《PEERJ COMPUTER SCIENCE》 (PeerJ Comput. Sci.)

年 卷 期：2025年第11卷

页      面：e2476-e2476页

核心收录：

基　　金：Qatar National Library 

主　　题：Breast cancer Mammography Artificial intelligence Medical imaging Health care 

摘      要：The integration of artificial intelligence into healthcare, particularly in mammography, holds immense potential for improving breast cancer diagnosis. Artificial intelligence (AI), with its ability to process vast amounts of data and detect intricate patterns, offers a solution to the limitations of traditional mammography, including missed diagnoses and false positives. This review focuses on the diagnostic accuracy of AI-assisted mammography, synthesizing findings from studies across different clinical settings and algorithms. The motivation for this research lies in addressing the need for enhanced diagnostic tools in breast cancer screening, where early detection can significantly impact patient outcomes. Although AI models have shown promising improvements in sensitivity and specificity, challenges such as algorithmic bias, interpretability, and the generalizability of models across diverse populations remain. The review concludes that while AI holds transformative potential in breast cancer screening, collaborative efforts between radiologists, AI developers, and policymakers are crucial for ensuring ethical, reliable, and inclusive integration into clinical practice.