Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans.
Dave Walker, senior director of revenue cycle, Radiology Associates of North Texas, explains how his practice uses artificial intelligence for revenue cycle management during the Radiology Business Management Association (RBMA) 2024 meeting.
All around the world, people are increasingly wise to the advance of AI. More than a few are growing ever more uneasy about it. And yet workers equipped with AI are both more productive and better at their jobs.
More than two-thirds of U.S. physicians have changed their minds about generative AI over the past year. In doing so, the re-thinkers have raised their level of trust in the technology to help improve healthcare.
Machine learning-based algorithms can predict how patients will respond to antidepressants, according to new research published in Nature Biotechnology. The secret, the authors revealed, is electroencephalography (EEG) data.
Deep learning-based AI models can improve the segmentation of white matter in 18F-FDG PET/CT images, according to a new study published in the Journal of Digital Imaging. This helps radiologists with the early diagnosis of neurodegenerative disease.
President Trump’s 2021 budget proposal included significant cuts related to healthcare and science research, but AI received additional funding compared to 2020.
Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans.
Dave Walker, senior director of revenue cycle, Radiology Associates of North Texas, explains how his practice uses artificial intelligence for revenue cycle management during the Radiology Business Management Association (RBMA) 2024 meeting.
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 FFRCT alone. This approach is safe and feasible, researchers wrote, and the next step is to gather additional data.
![Advanced artificial intelligence (AI) models can evaluate cardiovascular risk in routine chest CT scans without contrast, according to new research published in Nature Communications.[1] In fact, the authors noted, the AI approach may be more effective at identifying issues than relying on guidance from radiologists. Representative non-contrast CT slices for two patients (left), with super-imposed segmentations (right). One artificial intelligence (AI) model was used to segment a cardiac mask.](/sites/default/files/styles/stack_views/public/2024-04/screen_shot_2024-04-23_at_10.44.32_am.png.webp?itok=vXt3hxDo)
