Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review.

Journal: BMJ open
Published Date: Oct 20, 2021

Abstract

INTRODUCTION: The application of artificial intelligence (AI) technologies as a diagnostic aid in healthcare is increasing. Benefits include applications to improve health systems, such as rapid and accurate interpretation of medical images. This may improve the performance of diagnostic, prognostic and management decisions. While a large amount of work has been undertaken discussing the role of AI little is understood regarding the performance of such applications in the clinical setting. This systematic review aims to critically appraise the diagnostic performance of AI algorithms to identify disease from cross-sectional radiological images of the abdominopelvic cavity, to identify current limitations and inform future research.

Authors

  • George E Fowler
    Centre for Surgical Research, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK george.fowler@bristol.ac.uk.
  • Rhiannon C Macefield
    Centre for Surgical Research, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
  • Conor Hardacre
    Centre for Surgical Research, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
  • Mark P Callaway
    Department of Clinical Radiology, Bristol Royal Infirmary, Bristol, UK.
  • Neil J Smart
    Exeter Surgical Health Services Research Unit (HeSRU), Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.
  • Natalie S Blencowe
    Centre for Surgical Research, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

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