Evaluating the Integration of AI-Risk Prediction Models in Breast Cancer Screening: Clinical & Systemic Effects
DOI:
https://doi.org/10.33178/SMJ.2026.4Keywords:
Breast Cancer, Screening, Artificial IntelligenceAbstract
Background: Age based mammography screening has served as the foundation of breast cancer (BC) detection programmes, demonstrating significant reduction in BC mortality. However, reliance on age alone fails to account for individual risk variation, which contributes to overdiagnosis, false positives and interval cancers.
Objectives: This review outlines the evolution from age-based to risk-stratified breast cancer screening, assessing the role of artificial intelligence and examining the evidence required for clinical implementation effectively.
Methods: Literature was identified by searching PubMed, Scopus, Web of Science Scopus, and Cochrane Library with relevant MeSH terms and keywords. Inclusion and exclusion criteria were applied, with title and abstract screening conducted to assess eligibility. Studies addressing breast cancer screening, AI based risk prediction, and frameworks published between 2007 and 2026 were prioritised, generating the articles used in this review.
Results:
Advances in the understanding of breast cancer risk factors, including family history, genetic predisposition, lifestyle and mammographic breast density, have driven the evolution towards risk stratified screening, enabling greater consideration of individual variability in disease presentation. AI has recently emerged as a supplement to screening with applications in both computer aided detention and mammography derived risk prediction modelling. Evidence suggests that AI systems may enhance cancer detection, improve accuracy of risk stratification and perform consistently among diverse populations. However, many barriers exist before AI can be incorporated into existing screening programmes, including the need for strong prospective evaluation and demonstration of consistent performance among different subgroups.
Conclusion:
AI driven risk prediction has the potential to refine breast cancer screening beyond age based approaches. The clinical implementation however, will require rigorous validation, attention to equity among the population and development of appropriate ethical frameworks.
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