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Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers’ perspectives.

Antwi, W. K., Botwe, B. O. and Akudjedu, T. N., 2021. Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers’ perspectives. Insights into Imaging, 12 (1), 80.

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DOI: 10.1186/s13244-021-01028-z

Abstract

Purpose Studies have documented the clinical potentials of artificial intelligence (AI) in medical imaging practice to improving patient care. This study aimed to qualitatively explore the perception of radiographers relating to the integration of AI in medical imaging practice in Africa. Methods The study employed a qualitative design using an open-ended online instrument administered between March and August 2020. Participants consisted of radiographers working within Africa during the time of the study. Data obtained were analysed using qualitative content analysis. Six themes of concerns were generated: expectant tool; career insecurity; cost of new technology, equipment preservation and data insecurity; service delivery quality; need for expanding AI awareness. Results A total of 475 valid responses were obtained. Participants demonstrated a positive outlook about AI in relation to clinical quality improvement, competent diagnosis, radiation dose reduction and improvement in research. They however expressed concerns relating to the implementation of this technology, including job security and loss of core professional radiographer skills and roles. In addition, concerns regarding AI equipment maintenance, lack of awareness about AI and education and training opportunities were evident. Conclusion Awareness of the importance of AI in medical imaging practice was acknowledged; however, concerns relating to job security, data protection must be given critical attention for successful implementation of these advanced technologies in medical imaging in Africa. Inclusion of AI modules in the training of future radiographers is highly recommended.

Item Type:Article
ISSN:1869-4101
Uncontrolled Keywords:Artificial intelligence; Medical imaging; Radiography; Africa; Online surveys; Qualitative study
Group:Faculty of Health & Social Sciences
ID Code:35656
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:18 Jun 2021 10:25
Last Modified:21 Jun 2021 14:00

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