Virtual formats can benefit quality improvement initiatives by enabling leaders across institutions to collaborate on multiple projects while still offering their local teams support. 

Charles E. Kahn, Jr., MD, MS, editor of the the RSNA  journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine, discusses the need to validate AI algorithms with your own patient population data.  

The research revealed that 7.7% of patients screened for pancreatic cancer had their tumors missed on initial imaging exams but were diagnosed with cancer between three and 18 months later.  

Nearly 30% of corresponding authors included in the analysis shared that they had witnessed scientific fraud in their department within the last five years.

A new paper in Radiology explores factors that can lead to reader variability in CT imaging, from the radiologist’s experience level and subspecialty to navigation patterns and time spent interpreting. 

Researchers believe the new method of assessing treatment responses could pave the way for developing new cancer therapeutics.

When exam volumes increase, so too does the risk of diagnostic errors in interpretations, many of which can be clinically significant.

Experts found that compared to the average effective doses of organs, breast doses were higher, representing increased risks of breast cancer for women.