Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE).

Journal: European radiology
Published Date: Apr 14, 2022

Abstract

OBJECTIVE: There has been a large amount of research in the field of artificial intelligence (AI) as applied to clinical radiology. However, these studies vary in design and quality and systematic reviews of the entire field are lacking.This systematic review aimed to identify all papers that used deep learning in radiology to survey the literature and to evaluate their methods. We aimed to identify the key questions being addressed in the literature and to identify the most effective methods employed.

Authors

  • Brendan S Kelly
    St Vincent's University Hospital, Dublin, Ireland. brendanskelly@me.com.
  • Conor Judge
    Wellcome Trust - HRB, Irish Clinical Academic Training, Dublin, Ireland.
  • Stephanie M Bollard
    Wellcome Trust - HRB, Irish Clinical Academic Training, Dublin, Ireland.
  • Simon M Clifford
    St Vincent's University Hospital, Dublin, Ireland.
  • Gerard M Healy
    St Vincent's University Hospital, Dublin, Ireland.
  • Awsam Aziz
    School of Medicine, University College Dublin, Dublin, Ireland.
  • Prateek Mathur
    Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
  • Shah Islam
    Division of Brain Sciences, Imperial College London, GN1 Commonwealth Building, Hammersmith Hospital, Du Cane Road, London, W12 0HS, UK.
  • Kristen W Yeom
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Aonghus Lawlor
    Insight SFI Centre for Data Analytics, University College Dublin, Dublin, Ireland.
  • Ronan P Killeen
    St Vincent's University Hospital, Dublin, Ireland.

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