How far have we come? Artificial intelligence for chest radiograph interpretation.

Journal: Clinical radiology
Published Date: Jan 28, 2019

Abstract

Due to recent advances in artificial intelligence, there is renewed interest in automating interpretation of imaging tests. Chest radiographs are particularly interesting due to many factors: relatively inexpensive equipment, importance to public health, commonly performed throughout the world, and deceptively complex taking years to master. This article presents a brief introduction to artificial intelligence, reviews the progress to date in chest radiograph interpretation, and provides a snapshot of the available datasets and algorithms available to chest radiograph researchers. Finally, the limitations of artificial intelligence with respect to interpretation of imaging studies are discussed.

Authors

  • K Kallianos
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • J Mongan
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • S Antani
    National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
  • T Henry
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • A Taylor
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • J Abuya
    Department of Radiology, School of Medicine, College of Health Sciences, Moi University, Eldoret, Kenya.
  • M Kohli
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA. Electronic address: marc.kohli@ucsf.edu.

Keywords

External Resources

Popular Topics
Recent Journals