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In her presentation, Noushin Yahyavi, MD, from the University of Maryland in Baltimore discussed ways that stakeholders in imaging AI, including radiology departments and AI vendors, can better promote health equity with the technology. Biased data results in biased AI models,” Yahyavi said.
In a retrospective review of over 110 million imaging claims for patients with commercial insurance or Medicare Advantage, researchers noted key trends signaling significant increases in imaging billed by non-physician practitioners (NPPs).
Teleradiology can connect local healthcare facilities to a network of radiologists, providing quick access to expert interpretations and diagnoses. Reduced Turnaround Time : Teleradiology allows for faster imageinterpretation and reporting, which is critical in emergency situations.
in managing images, and several components have evolved. Standardization of medical image formats via DICOM (Digital Imaging and Communications in Medicine) in the 1990s fueled experimentations in medical imageinterpretation and analyses [iii].
Multiple factors could push growth higher, such as an aging population requiring more imaging paired with heightened fears of malpractice lawsuits in emergency departments. In fact, AI itself may paradoxically work to both expand and contract imaging volumes.
While the USPSTF recommends biennial screening, many insurance plans recognize the benefits of early detection and offer annual screening coverage. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis imageinterpretation: a multi-reader multi-case study. link] van Winkel, S.L.,
The authors also raise concerns about the emerging use of Artificial Intelligence (AI) support tools for imageinterpretation in mammography. While AI algorithms show promise in enhancing cancer detection, their impact on patient outcomes remain uncertain.
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