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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.
Stogiannos, Nikolaos; Litosseliti, Lia; O'Regan, Tracy; Scurr, Erica; Barnes, Anna; Kumar, Amrita; Malik, Rizwan; Pogose, Michael; Harvey, Hugh; McEntee, Mark F; Malamateniou, Christina.
Afiliación
  • Stogiannos N; Department of Radiography, City, University of London, UK; Magnitiki Tomografia Kerkyras, Greece. Electronic address: nikos.stogiannos@city.ac.uk.
  • Litosseliti L; School of Health & Psychological Sciences, City, University of London, UK. Electronic address: l.litosseliti@city.ac.uk.
  • O'Regan T; The Society and College of Radiographers, London, UK. Electronic address: tracyo@sor.org.
  • Scurr E; The Royal Marsden NHS Foundation Trust, UK. Electronic address: erica.scurr@rmh.nhs.uk.
  • Barnes A; King's Technology Evaluation Centre (KiTEC), School of Biomedical Engineering & Imaging Science, King's College London, UK. Electronic address: anna.barnes@kcl.ac.uk.
  • Kumar A; Frimley Health NHS Foundation Trust, UK. Electronic address: amrita.kumar@nhs.net.
  • Malik R; Bolton NHS Foundation Trust, UK. Electronic address: rizwan.malik@boltonftnhs.uk.
  • Pogose M; Hardian Health, UK. Electronic address: mike@hardianhealth.com.
  • Harvey H; Hardian Health, UK. Electronic address: hugh@hardianhealth.com.
  • McEntee MF; Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland. Electronic address: mark.mcentee@ucc.ie.
  • Malamateniou C; Department of Radiography, City, University of London, UK; European Society of Medical Imaging Informatics, Vienna, Austria. Electronic address: christina.malamateniou@city.ac.uk.
Int J Med Inform ; 186: 105423, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38531254
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.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Inteligencia Artificial Límite: Humans País/Región como asunto: Europa Idioma: En Revista: Int J Med Inform Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Inteligencia Artificial Límite: Humans País/Región como asunto: Europa Idioma: En Revista: Int J Med Inform Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article