FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies.

Journal: Academic radiology
Published Date: Dec 27, 2021

Abstract

RATIONALE AND OBJECTIVES: To assess key trends, strengths, and gaps in validation studies of the Food and Drug Administration (FDA)-regulated imaging-based artificial intelligence/machine learning (AI/ML) algorithms.

Authors

  • Shadi Ebrahimian
  • Mannudeep K Kalra
  • Sheela Agarwal
    Lenox Hill Radiology, New York, New York.
  • 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.
  • Mona Elkholy
    ACR Data Science Institute, Reston, Virginia.
  • Christoph Wald
    Chairman, Department of Radiology at Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School; Chair of the ACR Informatics Commission.
  • Bibb Allen
    Department of Radiology, Grandview Medical Center, Birmingham, Alabama. Electronic address: bibb@mac.com.
  • 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.

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