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.

A team of U.S. researchers found the brains of patients diagnosed with severe mental illnesses—such as schizophrenia and depression—may have more in common than previously thought, according to a recent fMRI study.

Aidoc has gained FDA approval for its new AI-powered solution that helps radiologists flag and triage urgent pulmonary embolism (PE) cases.

A deep learning-based diagnosis system can identify ACL tears in MRI scans as well as radiologists, according to a new study published in Radiology: Artificial Intelligence.

An artificial intelligence “super brain” could help eliminate unnecessary diagnostic testing in patients who present with stable chest pain, according to a recent study, potentially saving physicians and patients significant time and money.