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Radiology Staff Experiences With Integrating Artificial Intelligence Into Radiology Practice in a Swedish Hospital:
Per Nilsen1,2, Petra Svedberg1, Ingrid Larsson1
1School of Health and Welfare, Halmstad University, Box 823 Halmstad, Halmstad, 30118, Sweden, 46 0706341151.
Background:
The integration of artificial intelligence (AI) in radiology has advanced significantly, but research on how it affects the daily work of radiology staff is limited.
Objective:
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.
Methods:
This qualitative case study was conducted in a single radiology department with 40 employees in 1 hospital in southwestern Sweden. The study concerned the integration of an AI-powered medical imaging software designed to assist radiologists in analyzing and interpreting medical images. 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:
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 and to address various challenges and problems.
Conclusions:
The findings illustrate how AI integration was experienced in 1 hospital. While not generalizable, the study provides insights that may be useful for similar settings. Radiology staff believed AI integration enhanced decision-making and quality of care, but they encountered challenges from preadoption to routine use of AI in radiology practice. Strategies such as internal training and workflow adaptation facilitated the successful integration of AI in radiology.
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