Background Artificial intelligence (AI) is increasingly being used in medical imaging
analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of …

As the demand for quality healthcare increases, healthcare systems worldwide are
grappling with time constraints and excessive workloads, which can compromise the quality …

The clinical deployment of artificial intelligence (AI) applications in medical imaging is
perhaps the greatest challenge facing radiology in the next decade. One of the main …

Artificial intelligence has the potential to transform many application domains fundamentally.
One notable example is clinical radiology. A growing number of decision-making support …

DNA methylation aging clocks have become an invaluable tool in biogerontology research
since their inception in 2013. Today, a variety of machine learning approaches have been …

An accurate differential diagnosis (DDx) is a cornerstone of medical care, often reached
through an iterative process of interpretation that combines clinical history, physical …

Artificial intelligence algorithms based on principles of deep learning (DL) have made a
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …

In many real world medical image classification settings, access to samples of all disease
classes is not feasible, affecting the robustness of a system expected to have high …

This report presents an overview of how machine learning is rapidly advancing clinical
translational imaging in ways that will aid in the early detection, prediction, and treatment of …

Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned
with the development of computational models to train artificial intelligence systems. DL …