Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

Journal: Radiology
Published Date: Aug 2, 2022

Abstract

As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.

Authors

  • Dania Daye
    Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA. Electronic address: ddaye@mgh.harvard.edu.
  • Walter F Wiggins
    Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (W.F.W., M.T.C., K.M., S.A.G., E.G., M.H.R., G.C.G., K.P.A.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (W.F.W., M.T.C., K.M., K.P.A.).
  • Matthew P Lungren
  • Tarik Alkasab
    Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Nina Kottler
    Radiology Partners, El Segundo, California. Electronic address: nina.kottler@radpartners.com.
  • Bibb Allen
    Department of Radiology, Grandview Medical Center, Birmingham, Alabama. Electronic address: bibb@mac.com.
  • Christopher J Roth
    Chair, SIIM Program Committee; Chair, HIMSS-SIIM; Enterprise Imaging Community; Chair, ACR Informatics Commission; Chair, IHE Radiology (RSNA); RSNA Annual Meeting Program Planning Committee, Chair, Informatics Subcommittee; RSNA Informatics Committee; and Duke University School of Medicine, Durham, North Carolina.
  • Bernardo C Bizzo
    Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Mass General Brigham Data Science Office (DSO), Boston, MA, United States.
  • Kimberly Durniak
    From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A., B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham, NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford, Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.); and Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.).
  • James A Brink
  • David B Larson
    Department of Radiology, Warren Alpert Medical School, Brown University, 593 Eddy St, Providence, RI 02903 (I.P.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (I.P.); Visiana, Hørsholm, Denmark (H.H.T.); Department of Radiology, Stanford University, Palo Alto, Calif (S.S.H., D.B.L.); and Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.).
  • Keith J Dreyer
    Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Mass General Brigham Data Science Office (DSO), Boston, MA, United States.
  • Curtis P Langlotz
    Stanford University, University Medical Line, Stanford, CA, 94305, US.

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