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https://www.acr.org/Clinical-Resources/Clinical-Decision-Support
See other qualified Decision Support Mechanisms approved by CMS. Take part in the CMS-funded Radiology Support, Communication and Alignment Network (R-SCAN™) — administered by the ACR – with your referring providers. Use R-SCAN to get familiar with CDS/AUC, get CME Credit and get ABR maintenance of certification Part 4 Credit.
https://www.online-sciences.com/robotics/artificial-intelligence-in-radiology-decision-support-systems-medical-imaging-healthcare/
Decision support systems can use techniques such as rule-based reasoning, artificial neural networks, hypertext, Bayesian networks, and case-based reasoning, AI tools can aid decision making in diagnostic radiology, As these systems become a part of routine clinical care, it is important that radiologists understand the strengths and weaknesses ...
https://www.itnonline.com/article/intro-clinical-decision-support-radiology
Mar 19, 2013 · For a 2017 update on government requirements on clinical decision support watch the VIDEO "Clinical Decision Support Requirements for Cardiac Imaging." CDS for Radiology. In radiology, the main use of clinical decision support is to quickly determine what type of imaging exam or diagnostic testing is needed for a patient with specific symptoms.
https://pubs.rsna.org/doi/abs/10.1148/radiographics.14.4.7938772
These decision support systems use techniques such as rule-based reasoning, artificial neural networks, hypertext, Bayesian networks, and case-based reasoning. This article reviews these artificial intelligence techniques, describes their application in radiology, and discusses the role that decision support systems may play in radiology's future.Cited by: 82
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Appropriate-Use-Criteria-Program/CDSM
A Clinical Decision Support Mechanism (CDSM) is an interactive, electronic tool for use by clinicians that communicates appropriate use criteria (AUC) information to the user and assists them in making the most appropriate treatment decision for a patient’s specific clinical condition.
https://pubs.rsna.org/doi/10.1148/radiographics.14.4.7938772
These decision support systems use techniques such as rule-based reasoning, artificial neural networks, hypertext, Bayesian networks, and case-based reasoning. This article reviews these artificial intelligence techniques, describes their application in radiology, and discusses the role that decision support systems may play in radiology's future.Cited by: 82
https://www.aidoc.com/blog/radiology-decision-support/
Oct 29, 2019 · Technology and innovation are playing an increasingly relevant role in radiology decision support systems. The radiology profession is defined by its elegant understanding of complex images and scans, providing in-depth analysis and insights essential in patient care.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3473729/
Within radiology, the recent development of quantitative imaging techniques, such as perfusion imaging, and the development of imaging-based biomarkers in modern therapeutic assessment has highlighted the need for computer systems to provide the radiological community with support for academic as well as clinical/translational applications.Cited by: 17
https://www.ncbi.nlm.nih.gov/pubmed/7938772
These decision support systems use techniques such as rule-based reasoning, artificial neural networks, hypertext, Bayesian networks, and case-based reasoning. This article reviews these artificial intelligence techniques, describes their application in radiology, and discusses the role that decision support systems may play in radiology's future.Cited by: 82
https://www.diagnosticimaging.com/pacs-and-informatics/what-radiology-practices-need-know-about-decision-support
Jul 14, 2016 · The American College of Radiology (ACR) has been developing their own appropriate use criteria for years, as part of the Imaging 3.0 initiative. In 2012, they contracted with the National Decision Support Company (NDSC) to begin licensing their appropriateness criteria to the market.Author: Deborah Abrams Kaplan
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