ArticleSA journal of radiology2025
Perceptions and attitudes towards AI among trainee and qualified radiologists at selected South African training hospitals.
Article in SA journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Exploring Perceptions of Leveraging AI to Improve Outcomes in Maternal, Sexual, and Reproductive Health in Sub-Saharan Africa: Exploratory Qualitative Study.Journal of medical Internet research · 2026Article
- Quantifying the AI readiness gap: An international, multidisciplinary assessment of artificial intelligence literacy in the radiation oncology community.Clinical and translational radiation oncology · 2026Article
- Artificial intelligence for surgical care in war-torn sudan: Feasibility, barriers, and ethical perspectives from a conflict zone.Surgery in practice and science · 2026Article
- Artificial intelligence and breast cancer screening in Serbia: a dual-perspective qualitative study among radiologists and screening-aged women.Frontiers in radiology · 2026Article
- Applying artificial intelligence to cardiac MRI to diagnose congenital heart disease in low-resource settings such as Sub-Saharan Africa.Communications medicine · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
Background: Artificial intelligence (AI) is transforming industries, but its adoption in healthcare, especially radiology, remains contentious. Objectives: This study evaluated the perceptions and attitudes of trainee and qualified radiologists towards the adoption of AI in practice. Method: A cross-sectional survey using a paper-based questionnaire was completed by trainee and qualified radiologists. Survey questions covered AI knowledge, perceptions, attitudes, and AI training in the registrar programme on a 3-point Likert scale. Results: A total of 100 participants completed the survey; 54% were aged 26-65 years and 61% were female, with none currently using AI in daily radiology practice. The majority (78%) of participants understood the basics and knew the role of AI in radiology. Most knew about AI from media reports (77%) and majority (95%) were never involved in AI training; only 3% of participants had no knowledge of AI at all. Participants agreed that AI could reliably detect pathological conditions (89%), reach reliable diagnosis (89%), improve daily work (78%), and 89% favoured AI practice; 89% believed that in the future, machine learning will not be independent of the radiologist. Participants were willing to learn (98%) and contribute towards advancing AI software (97%) and agreed that AI will improve the registrars' programme (97%), also noting that AI applications are as important as medical skills (87%). Conclusion: The findings suggest AI in radiology is in its infancy, with a need for educational programmes to upskill radiologists. Contribution: Participants were positive about AI implementation in practice and in the registrar learning programme.
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Registered trials
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