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, N., Litosseliti, L. 
ORCID: 0000-0002-3305-4713, O'Regan, T. , Scurr, E., Barnes, A., Kumar, A., Malik, R., Pogose, M., Harvey, H., McEntee, M. F. & Malamateniou, C. ORCID: 0000-0002-2352-8575 (2024).
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.
International Journal of Medical Informatics, 186,
article number 105423.
doi: 10.1016/j.ijmedinf.2024.105423
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.
| Publication Type: | Article |
|---|---|
| Additional Information: | © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Publisher Keywords: | Artificial intelligence, Governance, Medical imaging, Radiography, Radiology |
| Subjects: | H Social Sciences > HN Social history and conditions. Social problems. Social reform Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine |
| Departments: | School of Health & Psychological Sciences School of Health & Psychological Sciences > Midwifery & Radiography |
| SWORD Depositor: |
Available under License Creative Commons: Attribution International Public License 4.0.
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