Current Clinical Applications of Artificial Intelligence in Radiology and Their Best Supporting Evidence.

Journal: Journal of the American College of Radiology : JACR
Published Date: Nov 1, 2020

Abstract

PURPOSE: Despite tremendous gains from deep learning and the promise of artificial intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situations. Adoption of standards such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Consolidated Standards of Reporting Trials, and the Checklist for Artificial Intelligence in Medical Imaging is increasing to improve the peer-review process and reporting of AI tools. However, no such standards exist for product-level review.

Authors

  • Amara Tariq
    Department of Biomedical Informatics, Emory School of Medicine, Atlanta, Georgia. Electronic address: amara.tariq2@emory.edu.
  • Saptarshi Purkayastha
    Indiana University School of Informatics and Computing, Indianapolis, IN, United States.
  • Geetha Priya Padmanaban
    School of Informatics Computing, Indiana University Purdue University, Indianapolis, Indiana.
  • Elizabeth Krupinski
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
  • Hari Trivedi
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
  • Imon Banerjee
    Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA.
  • Judy Wawira Gichoya
    Department of Interventional Radiology, Oregon Health & Science University, Portland, Oregon; Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia.

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