Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …

This review gives an overview of the current state of deep learning research in breast cancer
imaging. Breast imaging plays a major role in detecting breast cancer at an earlier stage, as …

Multiparametric magnetic resonance imaging (mpMRI) has been shown to improve
radiologists' performance in the clinical diagnosis of breast cancer. This machine learning …

This paper aims to provide a review of the basis for application of AI in radiology, to discuss
the immediate ethical and professional impact in radiology, and to consider possible future …

Screening for breast cancer reduces breast cancer–related mortality and earlier detection
facilitates less aggressive treatment. Unfortunately, current screening modalities are …

The majority of advances in cancer therapy have been achieved based on developments in
public awareness, as well as novel diagnostic and therapeutic modalities. Treatment …

Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast
cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis …

Machine learning is increasingly integrated into clinical practice, with applications ranging
from pre-clinical data processing, bedside diagnosis assistance, patient stratification …

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been
very promising applications of AI in the field of medicine, including medical imaging, in vitro …

Significant advances in imaging analysis and the development of high-throughput methods
that can extract and correlate multiple imaging parameters with different clinical outcomes …