Academic radiology departments vary in how they handle second opinion consultations on outside studies, according to new research published in the American Journal of Roentgenology. A more uniform approach, the researchers argued, could help radiologists and patients alike.

The researchers found features were so highly affected by CT acquisition and reconstruction settings that a majority were “nonreproducible.”

Brain scans contain vast amounts of patient data, some as valuable as that within your DNA. In a recent opinion piece published in Wired, professor of radiology and biomedical imaging at Yale, Evan D. Morris, PhD, argued that we need to do more to protect these valuable images.

The Reason for exam Imaging Reporting and Data System (RI-RADS) is a new standardized system to grade imaging orders and may improve patient care as a whole, according to a new analysis published in the European Journal of Radiology.

Medical images and data from more than five million patients in the U.S. are left unsecured and vulnerable on the internet, according to an investigative report published Sept. 17 by ProPublica and German public broadcaster Bayerischer Rundfunk.

A team of researchers from Taiwan performed a first-of-its-kind external validation of four AI algorithms used to detect pulmonary nodules in chest x-rays, sharing their results in Clinical Radiology. The classifiers could help radiologists improve medical imaging care as a whole.

The ACR released a statement urging more nuanced conclusions should be drawn from a Sept. 3 study published by JAMA that found the use of medical imaging continues to grow despite efforts to curb overutilization.

A model utilizing natural language processing and machine learning can accurately detect radiology reports that demand follow-up imaging, reported researchers of a new study published in the Journal of Digital Imaging.

Radiology reports derived from structured brain tumor MRI reporting and data systems (BT-RADS) showed measurable improvements compared to free text reports, according to a new study published in Academic Radiology.

A machine learning algorithm can determine appropriate follow-up imaging based off of radiology reports, according to a new study published in the Journal of Digital Imaging. The technology may eventually be developed to automatically tell if a patient completed their follow-up exam.

Patients would like to maintain some control over what data they share and who they share it with, however, according to results of a new study published in JAMA Network Open.

An audio/visual reporting tool integrated into an emergency department’s musculoskeletal workflow can improve communication between radiologists and referring providers while making imaging findings easier to comprehend.