Abstract
According to existing literature, the integration of Artificial Intelligence (AI) into medical imaging presents both opportunities and challenges for diagnostic radiographers. AI applications such as image recognition, pattern analysis, and workflow automation are transforming medical imaging and radiation sciences (MIRS). However, these advancements have also generated uncertainty regarding their implications for professional practice, job security, and ethical standards. In South Africa, the implementation of AI in diagnostic radiography is still emerging, and limited research has been conducted to explore radiographers’ perceptions of this technological evolution.
This qualitative exploratory and descriptive study aimed to explore and describe diagnostic radiographers’ perceptions on the emergence of AI in diagnostic medical imaging within the Gauteng Province, in South Africa. The study population comprised diagnostic radiographers working in seven hospitals, four private and three public. A purposive sampling method was used to recruit diagnostic radiographers who had experience or exposure to AI in clinical practice. Data was collected through semi-structured face-to-face and virtual interviews, supported by field notes, and analysed thematically using ATLAS.ti 25 software (Archive for Technology, Life-World, and Everyday Language).
Findings from this study revealed three major themes: 1) diagnostic radiographers’ perceived impact of AI on medical imaging; 2) challenges and concerns regarding AI adoption; and 3) training, education, and preparedness for AI integration. Participants recognised AI’s potential to improve workflow efficiency, diagnostic accuracy, and patient outcomes. However, they also expressed apprehension about limited training opportunities, ethical dilemmas, and potential job displacement.
The study recommends structured AI education and training within radiography curricula, institutional readiness for AI implementation, and clear policy frameworks to ensure ethical and sustainable integration. Overall, the findings provide valuable insights into how radiographers can adapt to AI-driven transformation in South African healthcare.