Deep Learning in Breast Cancer Screening: A Critical Review of Translational Challenges and Future Directions

Deep learning is transforming breast cancer screening by automating mammogram analysis. However, the rapid evolution of model architectures necessitates a critical evaluation of the field's trajectory and its readiness for clinical translation. This systematic literature review, conducted in accordance with PRISMA guidelines, critically analyses a core set of 37 peer-reviewed studies published between 2022 and 2024, a period marked by the emergence of Vision Transformers and large-scale self-supervised learning. In total, our synthesis draws on 109 references to provide a comprehensive analysis of this evolving field. While Convolutional Neural Networks (CNNs) remain the dominant architecture, our analysis reveals a significant shift towards more complex, data-intensive models. This review synthesises findings across the deep learning pipeline, from data acquisition to model evaluation, to identify systemic challenges. We argue that the field faces two critical, intertwined challenges: a significant reproducibility issue stemming from a pervasive reliance on large, inaccessible private datasets that impedes independent validation of state-of-the-art claims, and a critical gap in algorithmic fairness. This gap is evidenced by a profound lack of performance reporting across key patient demographics (such as ethnicity and breast density), which poses a significant risk to health equity. Promising innovations in self-supervised learning and domain-specific pre-training offer pathways to mitigate data scarcity and improve model robustness. However, translating algorithmic potential into tangible clinical benefit will require a concerted effort to foster open science, implement rigorous evaluation of generalisability and fairness, and develop models that are not only accurate but also equitable and trustworthy.