Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study

• Published in: Journal of medical radiation sciences 2021-09, Vol.68 (3), p.260-268
• Main Authors: Botwe, Benard O., Antwi, William K., Arkoh, Samuel, Akudjedu, Theophilus N.
• Format: Article
• Language: English
• Subjects: Allied Health Personnel; Artificial Intelligence; Committees; Cross-Sectional Studies; Decision making; Demography; Ghana; Humans; Likert scale; Medical diagnosis; Medical imaging; Original; Participation; perspectives; Professional ethics; Professionals; Questionnaires; radiographer; Radiography; Social networks
• ISSN: 2051-3895; 2051-3909
• DOI: 10.1002/jmrs.460

Introduction The integration of artificial intelligence (AI) systems into medical imaging is advancing the practice and patient care. It is thought to further revolutionise the entire field in the near future. This study explored Ghanaian radiographers’ perspectives on the integration of AI into medical imaging. Methods A cross‐sectional online survey of registered Ghanaian radiographers was conducted within a 3‐month period (February‐April, 2020). The survey sought information relating to demography, general perspectives on AI and implementation issues. Descriptive and inferential statistics were used for data analyses. Results A response rate of 64.5% (151/234) was achieved. Majority of the respondents (n = 122, 80.8%) agreed that AI technology is the future of medical imaging. A good number of them (n = 131, 87.4%) indicated that AI would have an overall positive impact on medical imaging practice. However, some expressed fears about AI‐related errors (n = 126, 83.4%), while others expressed concerns relating to job security (n = 35, 23.2%). High equipment cost, lack of knowledge and fear of cyber threats were identified as some factors hindering AI implementation in Ghana. Conclusions The radiographers who responded to this survey demonstrated a positive attitude towards the integration of AI into medical imaging. However, there were concerns about AI‐related errors, job displacement and salary reduction which need to be addressed. Lack of knowledge, high equipment cost and cyber threats could impede the implementation of AI in medical imaging in Ghana. These findings are likely comparable to most low resource countries and we suggest more education to promote credibility of AI in practice. This study assessed the perspectives of radiographers on the integration of artificial intelligence (AI) into medical imaging (MI). The radiographers have a positive attitude towards the clinical application of AI in MI. However, some were concerned about AI‐related errors, job displacement and reduction of basic salary, and many perceived that the lack of knowledge, high equipment costs and cyber threat could affect the implementation of AI in MI in Ghana.