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 …

Data drift refers to differences between the data used in training a machine learning (ML)
model and that applied to the model in real-world operation. Medical ML systems can be …

Background Contrast-enhanced mammography (CEM) and abbreviated breast MRI (ABMRI)
are emerging alternatives to standard MRI for supplemental breast cancer screening …

Research into the potential benefits of artificial intelligence for comprehending the intricate
biology of cancer has grown as a result of the widespread use of deep learning and …

Objective Although artificial intelligence (AI) has demonstrated promise in enhancing breast
cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various …

The purpose of breast cancer screening is to find cancers early to reduce mortality and to
allow successful treatment with less aggressive therapy. Mammography is the gold standard …

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

Abstract Aim To overview Artificial Intelligence (AI) developments and applications in breast
imaging (BI) focused on providing person-centred care in diagnosis and treatment for breast …

In current clinical practice, it is difficult to predict whether a patient receiving neoadjuvant
chemotherapy (NAC) for breast cancer is likely to encounter recurrence after treatment and …

Objectives The aim of the study is to develop and evaluate the performance of a deep
learning (DL) model to triage breast magnetic resonance imaging (MRI) findings in high-risk …