Clinical Artificial Intelligence Applications in Radiology: Neuro.

Journal: Radiologic clinics of North America
Published Date:

Abstract

Radiologists have been at the forefront of the digitization process in medicine. Artificial intelligence (AI) is a promising area of innovation, particularly in medical imaging. The number of applications of AI in neuroradiology has also grown. This article illustrates some of these applications. This article reviews machine learning challenges related to neuroradiology. The first approval of reimbursement for an AI algorithm by the Centers for Medicare and Medicaid Services, covering a stroke software for early detection of large vessel occlusion, is also discussed.

Authors

  • Felipe Campos Kitamura
    From the Department of Radiology, Stanford University, 300 Pasteur Dr, MC 5105, Stanford, CA 94305 (S.S.H.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Boston, Mass (J.K.C.); Massachusetts General Hospital & Brigham and Women's Hospital Center for Clinical Data Science, Boston, Mass (A.B.M., K.A.); Department of Radiology, University of Toronto, Toronto, Ontario, Canada (A.B.); Department of Radiology, St. Michael's Hospital, Toronto, Ontario, Canada (M.C.); Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, RI (I.P.); Universidade Federal de Goiás, Goiânia, Brazil (L.A.P., R.T.S.); Universidade Federal de São Paulo, São Paulo, Brazil (N.A., F.C.K.); Visiana, Hørsholm, Denmark (H.H.T.); MD.ai, New York, NY (L.C.); Department of Radiology, Weill Cornell Medicine, New York, NY (G.S.) Department of Radiology, University of California-San Francisco, San Francisco, Calif (M.D.K.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.).
  • Ian Pan
    Warren Alpert Medical School, Brown University, Providence, RI.
  • Suely Fazio Ferraciolli
    DasaInova, Diagnósticos da América SA (Dasa), São Paulo, São Paulo, Brazil.
  • Kristen W Yeom
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Nitamar Abdala
    From the Department of Radiology, Stanford University, 300 Pasteur Dr, MC 5105, Stanford, CA 94305 (S.S.H.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Boston, Mass (J.K.C.); Massachusetts General Hospital & Brigham and Women's Hospital Center for Clinical Data Science, Boston, Mass (A.B.M., K.A.); Department of Radiology, University of Toronto, Toronto, Ontario, Canada (A.B.); Department of Radiology, St. Michael's Hospital, Toronto, Ontario, Canada (M.C.); Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, RI (I.P.); Universidade Federal de Goiás, Goiânia, Brazil (L.A.P., R.T.S.); Universidade Federal de São Paulo, São Paulo, Brazil (N.A., F.C.K.); Visiana, Hørsholm, Denmark (H.H.T.); MD.ai, New York, NY (L.C.); Department of Radiology, Weill Cornell Medicine, New York, NY (G.S.) Department of Radiology, University of California-San Francisco, San Francisco, Calif (M.D.K.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.).