From resistance to reliance: A human-centered analysis of the spectrum of radiologists' trust in AI

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.