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.

When physicians place orders for imaging examinations, they often leave out key information that could help the radiologist provide better patient care. The authors of a new analysis published in the European Journal of Radiology have proposed a standardized grading system, the Reason for exam Imaging Reporting and Data System (RI-RADS), that could combat this issue.

Natural language processing (NLP) and machine learning can help track when free-text radiology reports include follow-up imaging recommendations, according to a new study published in the Journal of Digital Imaging.

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.

Researchers have developed an algorithm that identifies if follow-up imaging recommendations are adhered to or not, sharing their findings 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.