Explainable AI for Mammography: A Review

Background: Breast cancer is one of the top causes of death for women worldwide with an incidence rate of about one in eight women. Mammographic screening allows earlier detection and offers the best chance of cure. The advances in artificial intelligence through Explainable AI, Machine Learning and Deep Learning have bettered diagnostic capacities but mammography remains the primary method for screening. Objectives: To introduce a structured comparative framework mapping XAI methods against datasets, model architectures, and reported performance which is absent in prior studies. Furthermore, the study incorporates emerging paradigms such as curriculum learning, federated learning, and case-based reasoning in mammography interpretation, which have not been jointly reviewed before. Method: This systematic scoping review examines the use of XAI in breast cancer identification and risk estimation. We performed systematic searches on Scopus, IEEE Explore, PubMed, Springer nature and Google Scholar following a systematic search strategy. The search was conducted through 2019 to early 2025, in peer‐reviewed studies of XAI methods in datasets of breast cancer. Findings: The implementation of Federated learning with hybrid ML methods enhances accuracy while preserving data privacy, while SHAP and LIME provide feature-based interpretability. Deep learning models like XGBoost and CNNs achieve high classification performance, and Grad-CAM aids radiologists by visually localizing tumours for better decision-making. Novelty: This review offers a comparative, metric-driven synthesis of XAI methods in mammography. It summarizes the comparison of datasets, model performance, and interpretability of the models. It also introduces emerging trends such as federated learning, curriculum learning, and human-centric AI, providing new perspectives on connecting algorithmic breakthroughs to clinical needs. Significance: XAI enhances the transparency, interpretability, fairness, and trustworthiness of the AI‐enabled health system and medical devices, and more importantly, quality of care and health outcomes. Keywords: Artificial Intelligence, Breast Cancer, Deep Learning, Explainable Artificial Intelligence (XAI), Grad-CAM, Interpretability, LIME, Machine Learning, SHAP, XGBoost