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The integration of artificial intelligence in medical imaging practice: Perspectives of African radiographers.

Botwe, B.O., Akudjedu, T.N., Antwi, W.K., Rockson, P., Mkoloma, S.S., Balogun, E.O., Elshami, W., Bwambale, J., Barare, C., Mdletshe, S., Yao, B. and Arkoh, S., 2021. The integration of artificial intelligence in medical imaging practice: Perspectives of African radiographers. Radiography. (In Press)

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DOI: 10.1016/j.radi.2021.01.008

Abstract

Introduction The current technological developments in medical imaging are centred largely on the increasing integration of artificial intelligence (AI) into all equipment modalities. This survey assessed the perspectives of African radiographers on the integration of AI in medical imaging in order to offer unique recommendations to support the training of the radiography workforce. Methods An exploratory cross-sectional online survey of radiographers working within Africa was conducted from March to August 2020. The survey obtained data about their demographics and perspectives on AI implementation and usage. Data obtained were analysed using both descriptive and inferential statistics. Results A total of 1020 valid responses were obtained. Majority of the respondents (n = 883,86.6%) were working in general X-ray departments. Of the respondents, 84.9% (n = 866) indicated that AI technology would improve radiography practice and quality assurance for efficient diagnosis and improved clinical care. Fear of job losses following the implementation of AI was a key concern of most radiographers (n = 625,61.3%). Conclusion Generally, radiographers were delighted about the integration of AI into medical imaging, however; there were concerns about job security and lack of knowledge. There is an urgent need for stakeholders in medical imaging infrastructure development and practices in Africa to start empowering radiographers through training programmes, funding, motivational support, and create clear roadmaps to guide the adoption and integration of AI in medical imaging in Africa. Implication for practice The current study offers unique suggestions and recommendations to support the training of the African radiography workforce and others in similar resource-limited settings to provide quality care using AI-integrated imaging modalities.

Item Type:Article
ISSN:0033-8281
Uncontrolled Keywords:Artificial intelligence; Medical imaging; Radiography; Africa; Online surveys
Group:Faculty of Health & Social Sciences
ID Code:35211
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:23 Feb 2021 08:01
Last Modified:23 Feb 2021 08:01

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