Background There is considerable interest in the potential use of artificial intelligence (AI)
systems in mammographic screening. However, it is essential to critically evaluate the …

Attempts to use computers to aid in the detection of breast malignancies date back more
than 20 years. Despite significant interest and investment, this has historically led to minimal …

Background Inclusion of mammographic breast density (BD) in breast cancer risk models
improves accuracy, but accuracy remains modest. Interval cancer (IC) risk prediction may be …

Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …

Millions of breast imaging exams are performed each year in an effort to reduce the
morbidity and mortality of breast cancer. Breast imaging exams are performed for cancer …

Simple Summary Breast cancer is the most common cancer, which resulted in the death of
700,000 people around the world in 2020. Various imaging modalities have been utilized to …

Purpose Deep learning techniques, including convolutional neural networks (CNN), have
the potential to improve breast cancer risk prediction compared to traditional risk models. We …

PURPOSE Artificial intelligence (AI) algorithms improve breast cancer detection on
mammography, but their contribution to long-term risk prediction for advanced and interval …

Objectives Digital breast tomosynthesis (DBT) can detect more cancers than the current
standard breast screening method, digital mammography (DM); however, it can substantially …

Background Retrospective studies have suggested that using artificial intelligence (AI) may
decrease the workload of radiologists while preserving mammography screening …