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.

Machine learning algorithms can classify free-text pathology reports at the organ level and are easily interpreted by human readers, according to an Aug. 7 study published in Radiology: Artificial Intelligence.

The medical imaging industry contributes billions of dollars to state economies, according to three new reports published June 12, by the Medical Imaging & Technology Alliance (MITA). The findings, experts say, provide more reason to permanently repeal the medical device tax.

“Because this language model can so rapidly adapt to existing training labels, these results should not be considered the final work but rather a foundation upon which iterative improvement can be performed,” wrote the author of a new study published in the Journal of the American College of Radiology.

“Despite the common discovery of the (incidental lung nodule) ILN, assessment of radiology reports for lung nodule incidence and guideline concordance of recommendations is understudied,” wrote authors of research published in the Journal of the American College of Radiology.