Radiographers’ perceptions of artificial intelligence in radiology: A qualitative study
Abstract
Objective Artificial intelligence (AI) tools are increasingly being utilized by radiographers, with the potential to reduce radiation dose, inter-operator variability, and examination time. Despite this, there is limited qualitative research on radiographers’ perspectives on AI, which need to be explored in order to understand how AI can be implemented in the daily work of radiographers. The aim of this study was to explore radiographers’ perceptions of AI in a radiological setting and its impact on their daily work. Methods The study was conducted at a medium-sized Swedish hospital. Seven radiographers who use AI tools in their daily work were interviewed using open-ended questions. Data was analysed using content analysis with an inductive approach. Results The radiographers identified several changes in their work following the implementation of AI tools, which affected both their professional role and daily tasks. While the radiographers acknowledged both current and potential benefits, such as streamlining procedures and improving the care pathway, they also expressed concerns regarding AI’s impact on their role and on the care process. They raised concerns about the educational requirements to use these tools effectively. Conclusion AI tools influence the radiographic profession at multiple levels and may reshape the future role of radiographers. While patient care seems to remain a central focus for radiographers, other aspects of their role may shift due to automation, potentially not reducing workload or streamlining workflows but instead shifting them towards a more supervisory role. Understanding the limitations of AI systems is essential if radiographers are to retain their professional expertise.
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BibTeX
@article{msc2026radiographers,
title = {{Radiographers’ perceptions of artificial intelligence in radiology: A qualitative study}},
author = {Ivan Jelisic MSc and Anna Bjällmark PhD and Ulrika Arvidson BSc and Hermon Tezare BSc and May Bazzi PhD},
journal = {Digital Health},
year = {2026},
month = oct,
doi = {10.1177/20552076261501829},
url = {https://doi.org/10.1177/20552076261501829},
note = {CC BY},
}
RIS
TY - JOUR
TI - Radiographers’ perceptions of artificial intelligence in radiology: A qualitative study
AU - Ivan Jelisic MSc
AU - Anna Bjällmark PhD
AU - Ulrika Arvidson BSc
AU - Hermon Tezare BSc
AU - May Bazzi PhD
PY - 2026
DA - 2026/10/28
JO - Digital Health
DO - 10.1177/20552076261501829
UR - https://doi.org/10.1177/20552076261501829
AB - Objective Artificial intelligence (AI) tools are increasingly being utilized by radiographers, with the potential to reduce radiation dose, inter-operator variability, and examination time. Despite this, there is limited qualitative research on radiographers’ perspectives on AI, which need to be explored in order to understand how AI can be implemented in the daily work of radiographers. The aim of this study was to explore radiographers’ perceptions of AI in a radiological setting and its impact on their daily work. Methods The study was conducted at a medium-sized Swedish hospital. Seven radiographers who use AI tools in their daily work were interviewed using open-ended questions. Data was analysed using content analysis with an inductive approach. Results The radiographers identified several changes in their work following the implementation of AI tools, which affected both their professional role and daily tasks. While the radiographers acknowledged both current and potential benefits, such as streamlining procedures and improving the care pathway, they also expressed concerns regarding AI’s impact on their role and on the care process. They raised concerns about the educational requirements to use these tools effectively. Conclusion AI tools influence the radiographic profession at multiple levels and may reshape the future role of radiographers. While patient care seems to remain a central focus for radiographers, other aspects of their role may shift due to automation, potentially not reducing workload or streamlining workflows but instead shifting them towards a more supervisory role. Understanding the limitations of AI systems is essential if radiographers are to retain their professional expertise.
SN - 2055-2076
ER -