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

Abstract 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. Critical relevance statement This review critically evaluates the causes and consequences of over-detection and over-surveillance in breast cancer screening and highlights how AI can advance radiologic decision-making through improved lesion stratification and more efficient, personalised follow-up strategies. Key Points BI-RADS thresholds largely drive over-detection; refining downgrade rules for 3 and tightening biopsy in 4A may reduce unnecessary interventions without compromising cancer detection. Over-detection imposes burdens: unnecessary imaging and biopsies, psychosocial distress, economic costs, and environmental impact; its reduction enhances efficiency and patient safety. AI-assisted screening maintains or improves cancer detection while reducing recall rates and workload; it also enables risk-adapted management of BI-RADS 4A lesions, avoiding low-value procedures. Graphical Abstract