The adoption of AI in clinical practice is steadily growing, but how do radiology department heads perceive this technology? What are their key concerns and expectations for the future?
A recent survey of academic radiology department chairs from the United States reveals strong optimism about AI’s role in clinical radiology. Conducted among members of the Society of Chairs of Academic Radiology Departments (SCARD), the study highlights a clear prioritization of AI applications in image acquisition, interpretation workflow, and post-processing.
Despite this enthusiasm, cost remains a major barrier to implementation. Interestingly, while they assume most radiologists and technologists do not fear job displacement due to AI, they consider trainees to be slightly more concerned. Department chairs acknowledge AI’s potential to enhance efficiency, reduce burnout, and promote healthcare equity. These findings suggest that radiology leadership is not only focused on optimizing local workflows but also addressing broader systemic challenges in AI adoption.
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Artificial Intelligence in Radiology: A Leadership Survey
Journal of the American College of Radiology, 2025
Abstract
Purpose: Surveys to assess views about artificial intelligence (AI) of various diagnostic radiology constituencies have revealed interesting combinations of enthusiasm, caution, and implementation priorities. We surveyed academic radiology leaders about their views on AI and how they intend to approach AI implementation in their departments.
Materials and Methods: We conducted a web survey of Society of Chairs of Academic Radiology Departments (SCARD) members between October 5 and October 31, 2023 to solicit optimism or pessimism about AI, target use cases, planned implementation, and perceptions of their workforce. P-values are provided only for descriptive purposes and have not been adjusted for multiple testing in this exploratory research.
Results: The survey was sent to the 112 SCARD members and 43 responded (38%). Chairs were optimistic, with no statistical difference between views of AI in general versus generative AI. Chairs plan to implement AI to improve quality and efficiency (43/43, 100%), burnout (41/43, 95%), healthcare costs (22/43, 51%), and equity (27/43, 63%) and most likely will target the post-processing (26/43, 60%), interpretation workflow (26/43, 60%), and image acquisition (18/43,42%) steps in the imaging value chain. Chairs perceived that radiologists (36/43, 84%) and technologists (38/43, 88%) were not particularly worried about being displaced but saw trainees as slightly less confident (31/43, 72%). Free text responses revealed concerns about the cost of AI and emphasized trade-offs that needed to be balanced.
Conclusion: Radiology Chairs are optimistic about AI and poised to tackle departmental challenges. Concerns about generative AI and workforce replacement are minimal.