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Imaging AI in Practice: A Demonstration of Future Workflow Using Integration Standards
Walter F Wiggins1, Kirti Magudia1, Teri M Sippel Schmidt1
1Department of Radiology, Duke University School of Medicine, DUMC Box 3808, 2301 Erwin Rd, Durham, NC 27710 (W.F.W.); Department of Radiology & Biomedical Imaging, University of California, San Francisco, Calif (K.M., M.D.K.); Department of Biomedical Engineering, Marquette University, Milwaukee, Wis (T.M.S.S.); Departments of Biomedical Engineering (T.M.S.S.) and Radiology (S.D.O.), Medical College of Wisconsin, Milwaukee, Wis; Department of Informatics, Radiological Society of North America, Oak Brook, Ill (C.D.C.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (K.P.A.); and Mass General Brigham Center for Clinical Data Science, Boston, Mass (K.P.A.).
Abstract:
Artificial intelligence (AI) tools are rapidly being developed for radiology and other clinical areas. These tools have the potential to dramatically change clinical practice; however, for these tools to be usable and function as intended, they must be integrated into existing radiology systems. In a collaborative effort between the Radiological Society of North America, radiologists, and imaging-focused vendors, the Imaging AI in Practice (IAIP) demonstrations were developed to show how AI tools can generate, consume, and present results throughout the radiology workflow in a simulated clinical environment. The IAIP demonstrations highlight the critical importance of semantic and interoperability standards, as well as orchestration profiles for successful clinical integration of radiology AI tools. Keywords: Computer Applications-General (Informatics), Technology Assessment © RSNA, 2021.
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