Radiology’s Fight Against Prior Authorization Delays
ACR is leading national efforts to make prior authorization more efficient and clinically appropriate while reducing the administrative burden and supporting national legislation.
Read moreThe U.S. National Science Foundation (NSF) launched the National Artificial Intelligence Research Resource (NAIRR) pilot, including participation of the American College of Radiology® (ACR®) co-led Medical Imaging and Data Resource Center (MIDRC). NAIRR is a cross-federal agency initiative, partnering with 10 other federal agencies, and collaborating with the private, nonprofit and philanthropic sectors. The pilot is a first step toward achieving a shared research infrastructure that will strengthen and democratize access to critical resources necessary to power responsible artificial intelligence (AI) discovery and innovation. Included in the NAIRR Secure focus area, MIDRC will provide access to two networks of data, including the ACR COVID-19 Imaging Research Registry™ made up of more than 84,000 imaging studies and data from 37,000 patients.
The NAIRR pilot will provide advanced computing, datasets, models, software, training and user-support access to U.S.-based researchers and educators. Researchers can discover and apply for initial access to NAIRR pilot resources through the NAIRR pilot portal.
There are four focus areas of the NAIRR pilot:
For more information, contact Katie Grady, ACR Government Affairs Director.
Radiology’s Fight Against Prior Authorization Delays
ACR is leading national efforts to make prior authorization more efficient and clinically appropriate while reducing the administrative burden and supporting national legislation.
Read moreACR Supports Medicaid Coverage of Lung Cancer Screening
ACR-backed bill would mandate Medicaid lung cancer screening, expand cessation coverage, ban prior auth—aiming to save lives and reduce disparities.
Read moreAI-Powered Learning for Smarter Radiology Education
This article discusses the role of AI in radiology and how AI errors can be reframed as enhanced learning opportunities in radiology education.
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