Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience.

Journal: Journal of the American College of Radiology : JACR
Published Date: Mar 1, 2023

Abstract

The multitude of artificial intelligence (AI)-based solutions, vendors, and platforms poses a challenging proposition to an already complex clinical radiology practice. Apart from assessing and ensuring acceptable local performance and workflow fit to improve imaging services, AI tools require multiple stakeholders, including clinical, technical, and financial, who collaborate to move potential deployable applications to full clinical deployment in a structured and efficient manner. Postdeployment monitoring and surveillance of such tools require an infrastructure that ensures proper and safe use. Herein, the authors describe their experience and framework for implementing and supporting the use of AI applications in radiology workflow.

Authors

  • 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.
  • Giridhar Dasegowda
    Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Data Science Office, Mass General Brigham, Boston, Massachusetts.
  • Christopher Bridge
    Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Boston, MA 02129, USA.
  • Benjamin Miller
    Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
  • James M Hillis
    Digital Clinical Research Organization, Data Science Office, Mass General Brigham, Boston, Massachusetts.
  • Mannudeep K Kalra
  • 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.).
  • Markus Stout
    Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Data Science Office, Mass General Brigham, Boston, Massachusetts; Senior Director, Medical Imaging Informatics, Mass General Brigham, Boston, Massachusetts.
  • Thomas Schultz
    B-IT and Department of Computer Science, University of Bonn, 53115, Bonn, Germany. schultz@cs.uni-bonn.de.
  • Tarik Alkasab
    Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • 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.

Keywords

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