Finding such discrepancies is critical to the continuity of patient care, as medical records and reports are often utilized across multiple providers and facilities. 

This could be especially helpful when timely clinical decisions relative to the use of a contrast agent need to be made.

Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans. 

Apparently, it isn’t just humans who occasionally struggle to work under stress. According to a recent study, the performance of AI flounders, too. 

While the latest ChatGPT models show promise in easing the reporting burden on radiologists in the future, they aren't up to the task just yet.

The large language model can identify report errors seven times more quickly than human readers, new study reveals.

In a study, the most accurate model combined deep-learning with a radionomics approach.

An independent heart team blinded to ICA results was able to deliver helpful guidance for CABG procedures for 99.1% of patients using just CCTA and FFR_CT alone. This approach is safe and feasible, researchers wrote, and the next step is to gather additional data.