The American College of Radiology (ACR) has expanded its ACR AI-LAB pilot program geared toward helping radiologists develop AI models without the use of coding language.

PET brain imaging using a new brain imaging paradigm yields preliminary evidence that tobacco smokers may have reduced neuroimmune function compared with non-smokers.

A convolutional neural network (CNN) approach can accurately identify and sub-classify suspected tuberculosis (TB) on chest radiographs, according to research presented at the Society for Imaging Informatics in Medicine (SIIM) annual meeting.

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.

Machine learning (ML) can help healthcare providers predict heart disease—including heart attacks—better than other popular risk models, according to new research published in Radiology.

As AI technologies continue to evolve, they may be able to make a significant impact on patient care by reducing the amount of time physicians spend sorting through paperwork and documentation.

“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.