Imaging Informatics

Most parents of pediatric patients undergoing imaging examinations want those results as quickly as possible, even if it means not discussing them in person with a physician, according to new findings published in Academic Radiology.

Reviewing chest CT examinations with an “all-in-one” (AIO) window does not have a negative impact on radiologist performance, according to new findings published in Diagnostic and Interventional Imaging.

RSNA and the American College of Radiology (ACR) are launching a new clinical data registry to collect 3D printing data used in radiology.

Physicians frequently leave out key information when ordering imaging examinations, an oversight that can make it harder for the radiologist to do their job. A team of researchers worked to reverse that trend, sharing its findings in a new study published in the American Journal of Roentgenology.

Researchers continue to test the effectiveness of CDS, but according to a new commentary published in the American Journal of Roentgenology (AJR), it remains largely unknown how these systems will truly impact patient care.

Clinical decision support (CDS) tools can improve the appropriateness of advanced imaging orders, according to new findings published in the American Journal of Roentgenology. 

For radiology to truly benefit from AI’s potential, the specialty must learn how to get the most information possible out of all available digital data.

Diagrams and an easy-to-use website can help improve communication between referring physicians and radiologists, according to a new study published in Current Problems in Diagnostic Radiology.

Telemedicine makes more and more of an impact on healthcare in the United States with each passing year, and teleradiology is certainly an important part of that trend. 

As AI technologies continue to evolve, they may be able to make a significant impact on patient care by reducing the amount of time physicians spend sorting through paperwork and documentation.

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.