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.
Given the speed at which generative AI has penetrated every major sector of human endeavor, no expert in any field should pretend to know how to cleanly separate the disruptors from the disrupted.
As healthcare AI opens new avenues to improve care quality without unduly increasing operational costs, the technology also expands potential exposure to civil and criminal liabilities. And that’s not only for providers but also payers and suppliers.
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)
