Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study.

Abstract

Authors

• Jarrel C Y Seah
  From the Department of Radiology, Alfred Hospital, 55 Commercial Rd, Melbourne, Victoria 3004, Australia (J.C.Y.S., A.F.D.); Department of Radiology, Royal Melbourne Hospital, Melbourne, Australia (J.S.N.T., F.G.); Melbourne, Australia (A.K.); and Department of Radiology, Melbourne University, Melbourne, Australia (F.G.).
• Cyril H M Tang
  Annalise.ai, Sydney, NSW, Australia.
• Quinlan D Buchlak
  School of Medicine, The University of Notre Dame, Sydney, NSW, Australia. quinlan.buchlak1@my.nd.edu.au.
• Xavier G Holt
  Annalise.ai, Sydney, NSW, Australia.
• Jeffrey B Wardman
  Annalise.ai, Sydney, NSW, Australia.
• Anuar Aimoldin
  Annalise.ai, Sydney, NSW, Australia.
• Nazanin Esmaili
  School of Medicine, The University of Notre Dame, Sydney, NSW, Australia.
• Hassan Ahmad
  IBM Almaden Research Center, San Jose, CA.
• Hung Pham
  VA Palo Alto Health Care System, Livermore, CA, USA; Stanford University, CA, USA.
• John F Lambert
  Annalise.ai, Sydney, NSW, Australia.
• Ben Hachey
  Annalise.ai, Sydney, New South Wales, Australia.
• Stephen J F Hogg
  Annalise.ai, Sydney, NSW, Australia.
• Benjamin P Johnston
  Annalise.ai, Sydney, NSW, Australia.
• Christine Bennett
  School of Medicine, The University of Notre Dame, Sydney, NSW, Australia.
• Luke Oakden-Rayner
  Department of Medical Imaging Research, Royal Adelaide Hospital, Adelaide, Australia.
• Peter Brotchie
  St Vincent's Hospital, Melbourne, Victoria, Australia.
• Catherine M Jones
  I-MED Radiology Network, Brisbane, Queensland, Australia.

Keywords

External Resources