Lessons on AI implementation from senior clinical practitioners: An exploratory qualitative study in medical imaging and radiotherapy in the UK.

Journal: Journal of medical imaging and radiation sciences
PMID: 39579457

Abstract

INTRODUCTION: Artificial Intelligence (AI) has the potential to transform medical imaging and radiotherapy; both fields where radiographers' use of AI tools is increasing. This study aimed to explore the views of those professionals who are now using AI tools.

Authors

  • Nikolaos Stogiannos
    Division of Midwifery and Radiography, School of Health Sciences, City University of London, London, UK.
  • Tracy O'Regan
    The Society and College of Radiographers, 207 Providence Square, Mill Street, London, UK.
  • Erica Scurr
    The Royal Marsden NHS Foundation Trust, UK. Electronic address: erica.scurr@rmh.nhs.uk.
  • Lia Litosseliti
    School of Health & Psychological Sciences, City, University of London, UK. Electronic address: l.litosseliti@city.ac.uk.
  • Michael Pogose
    Hardian Health, Bolton, United Kingdom.
  • Hugh Harvey
    Institute of Cognitive Neurosciences, University College London, Alexandra House, 17-19 Queen Square, Bloomsbury, London WC1N 3AZ, England.
  • Amrita Kumar
    Frimley Health NHS Foundation Trust, Frimley, United Kingdom.
  • Rizwan Malik
    Bolton NHS Foundation Trust, Farnworth, United Kingdom.
  • Anna Barnes
    King's Technology Evaluation Centre (KiTEC), School of Biomedical Engineering & Imaging Science, King's College London, London, United Kingdom.
  • Mark F McEntee
    Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, Ireland.
  • Christina Malamateniou
    Division of Midwifery and Radiography, School of Health Sciences, City University of London, London, UK.

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