An AI-optimized American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) improved risk stratification of thyroid nodules and may be easier for readers to use, according to a new study published in Radiology.

The deep learning platform, tested on more than 6,000 cases from the National Lung Cancer Screening Trial and Northwestern University, performed similarly to six radiologists.

Researchers at Google have developed a deep learning algorithm that uses data from CT scans to predict a patient’s risk of lung cancer, according to findings published in Nature Medicine.

A team of radiologists at the University of Washington School of Medicine in Seattle will utilize fMRI, along with formal evaluations, to study the effects of cannabis on infant brain development.

The method included three deep convolutional neural networks which outperformed five clinical radiologists, according to results of a study published in Radiology: Artificial Intelligence.

“The value of AI applications in medical care can only be confirmed when professional guidelines provide recommendations for their use in specific clinical settings and patient populations,” wrote Gerold Porenta, MD, PhD, in a recent commentary published in the Journal of Nuclear Medicine.

According to a new commentary in Radiology: Artificial Intelligence, radiologists have no reason to fear being replaced—as long as they are willing to embrace AI and adapt to these changing times.  

French researchers used MRI to capture 3D images of babies as they made their way through the birth canal, offering insights they hope can indicate which may have trouble during labor.