Integration of Artificial Intelligence in Mammographic Screening: Current Evidence, Clinical Benefits, and Implementation Challenges – A Systematic Review
DOI:
https://doi.org/10.83356/2026.rr.n19.22Keywords:
Artificial Intelligence, Deep Learning, Machine Learning, Mammography, Breast Cancer Screening, Systematic ReviewAbstract
Introduction: Artificial intelligence (AI) has shown potential to improve diagnostic performance and efficiency in mammographic screening. This systematic review aimed to synthesize the evidence on AI diagnostic performance, retrospective identification of interval cancers, impact on workload, and challenges associated with clinical implementation.
Methods: A systematic review was conducted according to the PRISMA 2020 guidelines. PubMed, Scopus, and Web of Science were searched for studies published between January 2015 and February 2026. Methodological quality was assessed using QUADAS-2, and a narrative synthesis was performed due to study heterogeneity.
Results: A total of 1,133 records were identified, of which 72 underwent full-text assessment and 55 were included. AI demonstrated diagnostic performance comparable to conventional double reading across different screening settings. Several studies showed the ability to retrospectively identify findings associated with interval cancers. Triage, selective-reading, and hybrid integration strategies showed potential to reduce human reading workload, although results varied across implementation models.
Discussion: Methodological heterogeneity, limited prospective evidence, and challenges related to external validation, clinical integration, cost-effectiveness, and regulation limit the generalizability of the findings.
Conclusion: AI has the potential to support mammographic screening and improve its efficiency. Clinical implementation requires robust external validation, continuous performance monitoring, and prospective evaluation of clinical, organizational, and economic impact.
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