A new KLAS report highlights providers’ imaging needs related to the cloud, strategies for implementation, perceptions of imaging vendors’ cloud solutions, and more.
Researchers developed an advanced AI model capable of extracting measurements from unprocessed CT images in seconds. It then uses those data to evaluate the patient's mortality risk if they underwent TAVR.
The new series touches on topics like the basics of reading reports, information on BI-RADS and LI-RADS, how to digest data specific to various anatomy, and more.
Despite the opportunistic screening capabilities afforded by artificial intelligence applications, primary care providers are hesitant to embrace the technology.
Example image from the study from a Lung cancer screening CT image in a 66-year-old male patient. It shows a sessile nodule with internal air in the left mainstem to left upper lobe bronchus (arrow) with a mean diameter of 10 mm. The nodule was assigned as Lung-RADS category 4A in the clinical report. (B) Follow-up CT image shows the lesion is resolved. Image courtesy of RSNA
Using the latest version was associated with improved diagnostic accuracy, researchers wrote in a new analysis.
This reduction protocol allows for acceptable lesion visualization while also providing a cautionary cushion when the safety of sequential contrast injections is in question.