Many institutions are making radiology reports available via online portals, but can patients actually understand the information in them?

The latest numbers on cardiovascular deaths put the focus on innovative ways to point the trend line down again.

“Although one would think that the radiology report plays a critical role in patient care, few studies have determined whether radiology reports are actually viewed by referrers," Sadaf Sahraian, with Johns Hopkins Medical Institution said.

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.

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.

“Substantial differences in report structure, content, length, and degree of detail provided by different radiologists can be a source of confusion and frustration for referrers and patients," wrote authors of a new study published in the American Journal of Roentgenology.