Background Improved breast cancer risk assessment models are needed to enable
personalized screening strategies that achieve better harm-to-benefit ratio based on earlier …

Breast cancer is one of the most common diseases in women; it can have long-term
implications and can even be fatal. However, early detection, achieved through recent …

Purpose To evaluate the trustworthiness of saliency maps for abnormality localization in
medical imaging. Materials and Methods Using two large publicly available radiology …

Building robust deep learning-based models requires large quantities of diverse training
data. In this study, we investigate the use of federated learning (FL) to build medical imaging …

Novel screening and diagnostic tests based on artificial intelligence (AI) image recognition
algorithms are proliferating. Some initial reports claim outstanding accuracy followed by …

Breast tumors are from the common infections among women around the world. Classifying
the various types of breast tumors contribute to treating breast tumors more efficiently …

Artificial intelligence (AI) shows potential to improve health care by leveraging data to build
models that can inform clinical workflows. However, access to large quantities of diverse …

There is limited access to effective cervical cancer screening programs in many resource‐
limited settings, resulting in continued high cervical cancer burden. Human papillomavirus …

The pace of regulatory clearance of artificial intelligence (AI) algorithms for radiology
continues to accelerate, and numerous algorithms are becoming available for use in clinical …

Deep learning (DL) algorithms have shown remarkable potential in automating various tasks
in medical imaging and radiologic reporting. However, models trained on low quantities of …