ArticleEuropean journal of radiology open2026
From resistance to reliance: A human-centered analysis of the spectrum of radiologists' trust in AI.
Article in European journal of radiology open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
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Abstract
Background: Artificial intelligence (AI) is increasingly applied in radiology, yet research has focused mostly on technical implementation, while human factors, particularly radiologists' trust, which is critical for adoption, remain underexplored. Methods: We conducted semi-structured interviews with 18 radiologists from two hospitals using a guideline covering five domains: user, system, developer, ethical, and patient factors. Interviews allowed participants to elaborate, revisit points, contradict themselves, and introduce unanticipated topics. Thematic analysis with systematic coding identified key themes while preserving contextual nuance. Results: All participants had prior AI experience. Most (61%, 11/18) were optimistic about AI's potential for specific tasks. Accuracy/reliability (94%, 17/18) and time-saving (100%, 18/18) were consistently highlighted as the most critical factors for trust and adoption. Usability, including intuitive interfaces and seamless PACS integration, was emphasized by most (72%, 13/18), while half (50%, 9/18) noted the importance of transparency. Institutional reputation influenced trust in the large majority (89%, 16/18), with preference for non-commercial or reputable entities. Other important factors included clinician involvement in system design (56%, 10/18) and peer-reviewed evidence of performance (61%, 11/18). Ethical considerations included retaining human oversight (83%, 15/18), protecting patient privacy (44%, 8/18), and ensuring institutional data ownership (33%, 6/18). Conclusions: Successful integration of AI in radiology requires attention to radiologists' perspectives and human factors throughout development, implementation, and use. The diversity and complexity of trust-related factors highlight the importance of human-centered approaches to AI adoption and the need for further research to guide effective implementation.
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