Natural language processing (NLP) has shown potential to extract measurements and their primary descriptors from radiology reports and provide them in a structured format, according to findings published in the Journal of Digital Imaging.

A new CT- and PET-imaging-based approach—one that entails applying big data to personalizing treatment protocols—is needed to better identify which head and neck carcinoma (HNC) patient subgroups respond to which specific therapies.

In a world of networked medical devices, it’s not hard to imagine a radiology-heavy cyberattack that is not only malicious but also ingenious.
 

Consolidation has become the name of the game for many private radiology practices, but not everyone wants in. Many unaffiliated groups still prefer the independent side of the playing field, defending their turf by contracting to provide imaging services to hospitals and health systems.

Just four months after appointing the first-ever chief health informatics officer at CMS, the position is vacant.

Natural language processing (NLP) can provide significant value by auditing all communications related to critical findings, according to a new study published in the Journal of the American College of Radiology.

Machine learning (ML) can help providers extract all relevant facts from radiology reports in real time, according to a new study published in the Journal of Digital Imaging.

A recent study validating the 2017 version of the ultrasound Liver Imaging Reporting and Data System (US LI-RADS) for detecting hepatocellular carcinoma (HCC) identified a few limitations in its scoring.