Researchers have developed a new technique, DeepPET, that uses deep learning to turn PET imaging data into high-quality images at a much faster rate than traditional methods. 

Artificial intelligence-based computer-aided detection (AI-CAD) software can help radiologists detect more cancers when interpreting mammograms, according to a new study published in the Journal of Digital Imaging.

"Our findings suggest that patients with higher prechemotherapy DHEAS levels had lower odds of developing self-perceived cognitive impairment,” wrote authors of a study published in Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy.

"The global obesity pandemic has not only led to a greater incidence of cardiovascular disease and type 2 diabetes, but has also coincided with a rise in brain diseases, such as accelerated cognitive decline and dementia,” wrote authors of an April 23 study published in Radiology.

“Our research demonstrates that deep-learning models integrating routine imaging scans obtained at multiple time points can improve predictions of survival and cancer-specific outcomes for lung cancer," wrote Hugo Aerts, PhD, in a recent study published in Clinical Cancer Research.

As researchers continue to make significant advances with artificial intelligence (AI) and deep learning, has the time come for radiologists to be concerned about their jobs?

GE Healthcare’s Deep Learning Image Reconstruction (DLIR) engine, designed to be used with its Revolution Apex CT solution, has gained FDA clearance.

The supply of radiation oncologists hasn’t kept up with the global demand for radiation therapy. But could experts from across the world help create an AI algorithm capable of closing that gap?