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Principles for enhancing trust in artificial intelligence systems among medical imaging professionals in Ghana: A
A Donkor1, D Kumi2, E Amponsah2
1Department of Medical Imaging, Faculty of Allied Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; IMPACCT (Improving Palliative, Aged and Chronic Care Through Clinical Research and Translation), Faculty of Health, University of Technology Sydney, Australia.
Introduction:
To realise the full potential of artificial intelligence (AI) systems in medical imaging, it is crucial to address challenges, such as cyberterrorism to foster trust and acceptance. This study aimed to determine the principles that enhance trust in AI systems from the perspective of medical imaging professionals in Ghana.
Methods:
An anonymous, online, nationwide cross-sectional survey was conducted. The survey contained questions related to socio-demographic characteristics and AI trustworthy principles, including "human agency and oversight", "technical robustness and safety", "data privacy, security and governance" and "transparency, fairness and accountability".
Results:
A total of 370 respondents completed the survey. Among the respondents, 66.5 % (n = 246) were diagnostic radiographers. Considerable number of respondents (n = 121, 32.7 %) reported having little or no understanding of how medical imaging AI systems work. Overall, 54.9 % (n = 203) of the respondents agreed or strongly agreed that each of the four principles was important to enhance trust in medical imaging AI systems, with a composite mean score of 3.88 ± 0.45. Transparency, fairness and accountability had the highest rating (4.27 ± 0.58), whereas the mean score for human agency and oversight was 3.89 ± 0.53. Technical robustness and safety as well as data privacy, security and governance obtained mean scores of 3.79 ± 0.61 and 3.58 ± 0.65, respectively.
Conclusion:
Medical imaging professionals in Ghana agreed that human agency, technical robustness, data privacy and transparency are important principles to enhance trust in AI systems; however, future plans including medical imaging AI educational interventions are required to improve AI literacy among medical imaging professionals in Ghana.
Implications For Practice:
The evidence presented should encourage organisations to design and deploy trustworthy medical imaging AI systems.
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