Radiologists’ Perspectives on AI Integration in Mammographic Breast Cancer Screening: A Mixed Methods Study

作者：Serene Si Ning Goh, Qin Xiang Ng, Felicia Jia Hui Chan, Rachel Sze Jen Goh, Pooja Jagmohan, Shahmir H. Ali, Gerald Choon‐Huat Koh · 发表于：Cancers · 年份：2025 · DOI：10.3390/cancers17213491 · 被引用次数：3 · 研究领域：AI in cancer detection、Artificial Intelligence in Healthcare and Education、Radiology practices and education

BACKGROUND/OBJECTIVES: Artificial intelligence (AI) is increasingly applied in breast imaging, with potential to improve diagnostic accuracy and reduce workload in mammographic breast cancer screening. However, real-world integration of AI into national screening programs remains limited, and little is known about radiologists' perspectives in Asian settings. This study aimed to explore radiologists' perceptions of AI adoption in Singapore's breast screening program, focusing on perceived benefits, barriers, and requirements for safe integration. METHODS: We conducted a mixed methods study involving a cross-sectional survey of 17 radiologists with prior experience using AI-assisted mammography, followed by semi-structured interviews with 10 radiologists across all three public healthcare clusters. The survey measured confidence in AI, attitudes toward its diagnostic role, and integration preferences. Interviews were analyzed thematically, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. RESULTS: Among survey respondents, 64.7% recommended AI as a companion reader, though only 29.4% rated its performance as comparable to humans. Confidence was highest when AI was validated on local datasets (mean 9.3/10). Interviews highlighted AI's strengths in routine, fatigue-prone tasks, but skepticism for complex cases. Concerns included false positives, workflow inefficiencies, medico-legal accountability, and long-term costs. Radiologists emphasized th...