In order to properly train and validate algorithms, developers need high volumes of quality-labeled data. But such datasets are not easy to obtain.

Predictions of heart attacks and deaths based on coronary computed tomography angiography (CCTA) are more accurate when made using an artificial intelligence (AI) algorithm than with the Coronary Artery Disease Reporting and Data System (CAD-RADS) or other risk assessment methods.

“In a scenario where double reading at screening mammography is not available…we believe that the use of this model as a second reader could be beneficial,” wrote researchers in a new study published by Radiology.

You can find just about anything in an app store. Soon, that may include artificial intelligence applications for radiologists, as a recent Harvard Business Review article suggested.

Artificial neural networks (ANNs) can help radiologists classify pure ground glass opacities (GGOs), according to a new study published in Clinical Imaging. But they shouldn't rely solely on AI-produced findings.

A new three-dimensional (3D) tissue imaging technique can help scientists noninvasively study cells and may lead to improved treatments for a variety of diseases, according to research published in eLife.

“Search for an aneurysm is one of the most labor-intensive and critical tasks radiologists undertake,” said co-senior author Kristen Yeom, MD, associate professor of radiology at Stanford University.

A new deep learning approach lowered radiation exposure from CT imaging while producing higher quality scans compared to traditional iterative reconstruction techniques, according to research published in Nature Machine Intelligence.