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Black box no more: A cross-sectional multi-disciplinary survey for exploring governance and guiding adoption of AI in medical imaging and radiotherapy in the UK

  • Nikolaos Stogiannos
  • , Lia Litosseliti
  • , Tracy O'Regan
  • , Erica Scurr
  • , Anna Barnes
  • , Amrita Kumar
  • , Rizwan Malik
  • , Michael Pogose
  • , Hugh Harvey
  • , Mark F. McEntee
  • , Christina Malamateniou
  • City, University of London
  • Magnitiki Tomografia Kerkyras
  • 207 Providence Square
  • Royal Marsden NHS Foundation Trust
  • King's College London
  • Frimley Health NHS Foundation Trust
  • Bolton NHS Foundation Trust
  • Hardian Health
  • European Society of Medical Imaging Informatics

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Medical Imaging and radiotherapy (MIRT) are at the forefront of artificial intelligence applications. The exponential increase of these applications has made governance frameworks necessary to uphold safe and effective clinical adoption. There is little information about how healthcare practitioners in MIRT in the UK use AI tools, their governance and associated challenges, opportunities and priorities for the future. Methods: This cross-sectional survey was open from November to December 2022 to MIRT professionals who had knowledge or made use of AI tools, as an attempt to map out current policy and practice and to identify future needs. The survey was electronically distributed to the participants. Statistical analysis included descriptive statistics and inferential statistics on the SPSS statistical software. Content analysis was employed for the open-ended questions. Results: Among the 245 responses, the following were emphasised as central to AI adoption: governance frameworks, practitioner training, leadership, and teamwork within the AI ecosystem. Prior training was strongly correlated with increased knowledge about AI tools and frameworks. However, knowledge of related frameworks remained low, with different professionals showing different affinity to certain frameworks related to their respective roles. Common challenges and opportunities of AI adoption were also highlighted, with recommendations for future practice.

Original languageEnglish
Article number105423
JournalInternational Journal of Medical Informatics
Volume186
DOIs
Publication statusPublished - Jun 2024

Keywords

  • Artificial intelligence
  • Governance
  • Medical imaging
  • Radiography
  • Radiology

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