Perceptions of Data Set Experts on Important Characteristics of Health Data Sets Ready for Machine Learning: A Qualitative Study.

Journal: JAMA network open
Published Date: Dec 1, 2023

Abstract

IMPORTANCE: The lack of data quality frameworks to guide the development of artificial intelligence (AI)-ready data sets limits their usefulness for machine learning (ML) research in health care and hinders the diagnostic excellence of developed clinical AI applications for patient care.

Authors

  • Madelena Y Ng
    Department of Medicine, Stanford University, Stanford, CA, USA.
  • Alaa Youssef
    Department of Radiology, Stanford School of Medicine, Stanford, CA, USA.
  • Adam S Miner
    Clinical Excellence Research Center, Stanford University, Stanford, California.
  • Daniela Sarellano
    Department of Radiology, Stanford University School of Medicine, Stanford, California.
  • Jin Long
    Center for Artificial Intelligence in Medicine and Imaging, Stanford University, 1701 Page Mill Road, Palo Alto, CA, 94304, USA.
  • 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.).
  • Tina Hernandez-Boussard
    Stanford Center for Biomedical Informatics Research, Stanford, California 94305, USA.
  • Curtis P Langlotz
    Stanford University, University Medical Line, Stanford, CA, 94305, US.

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