Value Proposition of FDA-Approved Artificial Intelligence Algorithms for Neuroimaging.

Journal: Journal of the American College of Radiology : JACR
PMID: 37574094

Abstract

PURPOSE: The number of FDA-cleared artificial intelligence (AI) algorithms for neuroimaging has grown in the past decade. The adoption of these algorithms into clinical practice depends largely on whether this technology provides value in the clinical setting. The objective of this study was to analyze trends in FDA-cleared AI algorithms for neuroimaging and understand their value proposition as advertised by the AI developers and vendors.

Authors

  • Suryansh Bajaj
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut.
  • Mihir Khunte
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut.
  • Nagaraj S Moily
    Visage Imaging, San Diego, California.
  • Seyedmehdi Payabvash
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
  • Max Wintermark
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Dheeraj Gandhi
    Department of Neurosurgery, University of Maryland Medical Center, Baltimore, MD, USA.
  • Ajay Malhotra
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, Box 208042, Tompkins East 2, 333 Cedar St, New Haven, CT, 06520-8042, USA. ajay.malhotra@yale.edu.

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