Is radiology ready for clinical decision support?
Starting in January 2020, the testing period will begin for new federal rules requiring medical imaging exams to be ordered using appropriate use criteria (AUC) and clinical decision support (CDS). The question is, are radiology practices ready?
That depends on whom you ask. While most in radiology are vaguely aware that AUC/CDS is looming, many aren't quite sure what it means. And indeed, some practices appear to be hoping that the U.S. Centers for Medicare and Medicaid Services (CMS) will revert to past habits and simply postpone the entire exercise.
But that's unlikely to happen. Instead, radiologists, administrators, and business managers should take advantage of the 2020 testing period to prepare for AUC/CDS. More importantly, they should reach out to referring physicians to ensure that they are ready as well. If they don't, they could start to see interruptions in Medicare reimbursement starting in 2021.
Why a Research-First Platform for Imaging Informatics and Machine Learning?
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It's no secret that researchers face many challenges that impede the research and development of artificial intelligence (AI) solutions in clinical settings. Machine learning requires large volumes of data for accuracy in most applications. Institutions often have a wealth of data but lack the systems needed to get it into the hands of researchers cost-effectively.
Those data must be of high quality and labeled correctly. Imaging projects often involve complex preprocessing to identify and extract features and biomarkers. To further complicate matters, security and privacy are critical, particularly when involving collaboration outside of the context of clinical care.
Unfortunately, established clinical solutions fail to address six critical needs of researchers, impeding research productivity and slowing innovation.
AI drives advances in imaging informatics in 2019
The adoption of deep learning-based image reconstruction methods, the proposal of an artificial intelligence (AI)-friendly regulatory policy, and new approaches aimed at training more robust AI models rank among the most important advances in 2019 for imaging informatics.
Dr. Eliot Siegel of the University of Maryland believes the implementation of deep learning in image reconstruction algorithms for use on commercial CT, MRI, and PET/CT scanners is the biggest development in imaging informatics in 2019.
"[This] will have a major impact over the next five years in reducing the amount of time required for MRI and PET/CT studies, will reduce the loss of texture which is associated with iterative reconstruction algorithms in CT, and will reduce the amount of radiation for CT and PET/CT substantially," Siegel told AuntMinnie.com. "New scanners are just beginning to incorporate deep-learning reconstruction, and third-party companies are offering this technology for older machines."