Over-detection and over-surveillance in breast screening: current status and the potential for artificial intelligence optimisation

作者：Siyu Wang, Jingyan Liu, Linlin Song, Wen Wen, Juan Huang, Yulan Peng · 发表于：Insights into Imaging · 年份：2025 · DOI：10.1186/s13244-025-02160-w · 被引用次数：4 · 研究领域：AI in cancer detection、Digital Radiography and Breast Imaging、Breast Lesions and Carcinomas

Breast screening reduces cancer-specific mortality but can also precipitate avoidable harms through over-detection of benign abnormalities and subsequent over-surveillance. Across mammography and digital breast tomosynthesis (DBT), ultrasound and magnetic resonance imaging (MRI), gains in sensitivity are often offset by reduced specificity, driving false-positive recalls, benign-biopsy burden and resource strain. Within breast imaging reporting and data system (BI-RADS)-guided decision-making, Category 3 and Category 4A trigger short-interval follow-up or biopsy despite low event rates, amplifying anxiety and cost. Artificial intelligence (AI) offers a practical route to mitigate these drawbacks. Prospective and real-world studies indicate that AI-assisted reading can maintain or improve cancer detection while lowering recall rates and workload. AI models also support finer risk stratification-particularly for BI-RADS 4 lesions-thereby reducing unnecessary interventions. This review synthesises evidence on the performance and limitations of mainstream screening technologies, delineates the multidimensional impact of over-detection, and evaluates the capacity of AI to rebalance sensitivity and specificity, optimise follow-up intervals and support risk-adapted workflows. A patient-centred, evidence-driven strategy that integrates validated AI with clearly defined decision thresholds and effective patient-provider communication can maximise benefit while minimising harm. CRITICA...