Accepted for/Published in: JMIR Formative Research
Date Submitted: May 21, 2025
Open Peer Review Period: Jun 9, 2025 - Aug 4, 2025
Date Accepted: Nov 17, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Integrating Artificial Intelligence into Radiology Practice: A Qualitative Interview Study of Swedish Radiology Staff Experiences
ABSTRACT
Background:
Background:
Integration of artificial intelligence (AI) in radiology has advanced significantly but research on how it affects the daily work of radiology staff is limited. This study aimed to explore the experiences of radiology staff on the integration of an AI application in a radiology department in Sweden. This understanding is essential for developing strategies to address potential challenges in AI integration and optimize the use of AI applications in radiology practice.
Objective:
Objective:
The aim of this study was to explore the experiences of radiology staff on the integration of an AI application in a radiology department in Sweden. The study seeks to provide insights into the real-world application of AI in radiology, with a particular focus on understanding the practical implications of AI integration based on the experiences of staff in a radiology department.
Methods:
Methods:
A study on the integration of AI-powered medical imaging software designed to assist radiologists in analyzing and interpreting medical images was conducted in a radiology department with 40 employees at a hospital in southwestern Sweden. Using a qualitative design, interviews were conducted with 7 radiologists (physicians), 4 radiologic technologists, and 1 physician assistant. Their experience within radiology varied between 13 months and 38 years. The data were analyzed using qualitative content analysis.
Results:
Results:
Participants cited numerous strengths and advantages of significant value in integrating AI into radiology practice. Numerous challenges were also revealed in terms of difficulties associated with choosing, acquiring, and deploying the AI application and operational issues in radiology practice. They discussed experiences with diverse strategies to facilitate the integration of AI in radiology to address various challenges and problems.
Conclusions:
Conclusions:
Radiology staff in Sweden benefited from AI integration, enhancing decision-making and quality of care. However, they encountered challenges from pre-adoption to routine use of AI in radiology practice. Strategies such as internal training and workflow adaptation can facilitate successful integration of AI in radiology. Clinical Trial: No
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.