Implementation of AI in radiology: the perspective of referring physicians

作者：Jennifer Gotta, Leon D. Grünewald, Vitali Koch, Scherwin Mahmoudi, Simon Bernatz, Elena Höhne, Teodora Biciusca, Aynur Gökduman, Christian Wolfram, Christian Booz, Jan‐Erik Scholtz, Simon S. Martin, Katrin Eichler, Tatjana Gruber‐Rouh, Andreas Bucher, İbrahim Yel, Thomas J. Vogl, Philipp Reschke · 发表于：Insights into Imaging · 年份：2025 · DOI：10.1186/s13244-025-02120-4 · 被引用次数：4 · 研究领域：Artificial Intelligence in Healthcare and Education、Explainable Artificial Intelligence (XAI)、Radiology practices and education

OBJECTIVES: AI offers considerable potential to improve diagnostic accuracy and efficiency in radiology. However, its successful implementation depends largely on the trust and acceptance of referring physicians. This study examines physicians' attitudes toward AI in radiology, identifying key facilitators and barriers to its clinical integration. MATERIALS AND METHODS: A total of 169 licensed physicians in Germany, including surgeons, internists, and general practitioners who frequently refer patients to radiology, were surveyed. Participants were recruited via a systematic review of hospital and practice websites. A structured online questionnaire assessed perceptions of AI, focusing on trust-related factors, preferred applications, and adoption barriers. Statistical analysis was conducted using R and Python. RESULTS: Overall, 60% of respondents evaluated AI positively for enhancing diagnostic accuracy (mean score 3.7 ± 1.2). The most influential trust factor was model transparency (56.3%), followed by legal clarity on liability (25.0%) and strong data protection (11.7%). Transparency was rated significantly higher than other factors (p < 0.001). Preferred AI applications included lesion detection, research data analysis, and workflow management. Barriers to adoption included the "black box" nature of AI, unclear accountability, and data privacy concerns. Subgroup analysis revealed no significant variation in trust factors between specialties (p = 0.21). CONCLUSION: Physici...