Is AI the Ultimate QA?

Journal: Journal of digital imaging
Published Date: Feb 15, 2022

Abstract

We are among the many that believe that artificial intelligence will not replace practitioners and is most valuable as an adjunct in diagnostic radiology. We suggest a different approach to utilizing the technology, which may help even radiologists who may be averse to adopting AI. A novel method of leveraging AI combines computer vision and natural language processing to ambiently function in the background, monitoring for critical care gaps. This AI Quality workflow uses a visual classifier to predict the likelihood of a finding of interest, such as a lung nodule, and then leverages natural language processing to review a radiologist's report, identifying discrepancies between imaging and documentation. Comparing artificial intelligence predictions with natural language processing report extractions with artificial intelligence in the background of computer-aided detection decisions may offer numerous potential benefits, including streamlined workflow, improved detection quality, an alternative approach to thinking of AI, and possibly even indemnity against malpractice. Here we consider early indications of the potential of artificial intelligence as the ultimate quality assurance for radiologists.

Authors

  • Edmund M Weisberg
    Professor, Johns Hopkins Medicine, Department of Radiology and Radiological Science, Department of Oncology and Department of Surgery; Director of Diagnostic Imaging and Body CT at Johns Hopkins University School of Medicine, Baltimore, Maryland; Senior Science Writer, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, Maryland; The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, Maryland. Electronic address: eweisbe1@jhmi.edu.
  • Linda C Chu
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: lindachu@jhmi.edu.
  • Benjamin D Nguyen
    Transcarent, Inc., 2 S Park St., FL. 1, San Francisco, CA, 94107, USA.
  • Pelu Tran
    FerrumFerrum Health, Santa Clara, CA, USA.
  • Elliot K Fishman
    The Russell H. Morgan Department of Radiology and Radiologic Science, Johns Hopkins School of Medicine, Baltimore, Maryland. Electronic address: efishman@jhmi.edu.

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