AI-Driven Microcalcification Detection in Digital Mammography for Early Breast Cancer Diagnosis: A Scoping Review, Challenges, Limitations, and Future Perspectives

Background: Microcalcifications (MCs) are among the earliest mammographic signs of breast cancer, yet their detection remains challenging due to small size, low contrast, and dense breast tissue. This scoping review synthesizes AI-driven methods for MC detection in digital mammography, focusing on three dimensions: comparative performance of deep learning (DL) versus traditional methods, the clinical impact of explainable artificial intelligence (XAI), and the role of synthetic data in addressing dataset limitations. Methods: Following PRISMA-ScR guidelines, we systematically searched seven databases for studies published between January 2000 and January 2026. Of 366 initial records, 72 peer-reviewed studies were included in the final synthesis. Results: DL architectures, particularly convolutional neural networks (CNNs), have generally reported higher diagnostic performance (accuracy up to 99.71% and Area Under the Curve (AUC) up to 0.998) than traditional machine learning methods, although direct comparisons are hindered by heterogeneous datasets and evaluation protocols. XAI techniques have yet to undergo rigorous validation in real-world clinical settings, with very low certainty of evidence regarding their impact on radiologists’ trust or workflow integration. Synthetic data generation mitigates some data scarcity and privacy constraints but introduces artifacts (e.g., checkerboard patterns in 39–46% of cases) that limit clinical realism. Conclusions: DL offers substantial promise for MC detection, but translation to clinical practice requires robust XAI validation, higher-quality synthetic data, and prospective studies on diverse, longitudinal datasets.