Explainable Artificial Intelligence (XAI) for Cancer Classification in Medical Imaging: A Systematic Review

Our study examines the growing role of Explainable Artificial Intelligence (XAI) in cancer medical imaging, where transparency and interpretability are essential for trustworthy clinical decision making. Using a PRISMA-guided systematic literature review, 926 records published between 2020 and 2026 were identified from major databases, with 46 studies meeting the inclusion criteria after screening and quality assessment. The review systematically analyzes XAI techniques, model architectures, evaluation approaches, interpretability mechanisms, challenges, and future research directions. The findings show that gradient-based methods, particularly Grad-CAM, dominate the field due to their ease of integration with convolutional neural networks. At the same time, complementary approaches such as LIME, SHAP, and Integrated Gradients provide additional attribution insights. Evaluation practices remain heterogeneous, with a strong reliance on qualitative visual inspection and limited standardized quantitative frameworks. XAI contributes to interpretability primarily through spatial localization, feature attribution, and clinical decision support; however, challenges persist, including instability in explanations, coarse localization, high computational cost, and limited compatibility with transformer-based models. Overall, while XAI enhances transparency in cancer imaging, its clinical reliability remains constrained by methodological and technical limitations. Future work should focus on standardized evaluation, clinician-centered validation, and the development of robust, multimodal, and architecture-aware explainability frameworks.