Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study.

Journal: BMJ open
Published Date: Dec 20, 2021

Abstract

OBJECTIVES: Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists.

Authors

  • Catherine M Jones
    I-MED Radiology Network, Brisbane, Queensland, Australia.
  • Luke Danaher
    I-Med Radiology Network, Sydney, New South Wales, Australia.
  • Michael R Milne
    Annalise-AI, Sydney, New South Wales, Australia michael.milne@annalise.ai.
  • Cyril Tang
    Annalise-AI, Sydney, New South Wales, Australia.
  • Jarrel Seah
    Department of Neuroscience, Monash University, Melbourne, Australia; Radiology and Nuclear Medicine, Alfred Health, Melbourne, Australia.
  • Luke Oakden-Rayner
    Department of Medical Imaging Research, Royal Adelaide Hospital, Adelaide, Australia.
  • Andrew Johnson
    Annalise-AI, Sydney, New South Wales, Australia.
  • Quinlan D Buchlak
    School of Medicine, The University of Notre Dame, Sydney, NSW, Australia. quinlan.buchlak1@my.nd.edu.au.
  • Nazanin Esmaili
    School of Medicine, The University of Notre Dame, Sydney, NSW, Australia.

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