The field of deep learning is moving towards a direction where interpretability and explainability are becoming essential components of model development. This shift is driven by the need for transparency and trust in critical applications such as medical imaging and diagnostics. Recent developments have focused on incorporating explainable AI techniques into deep learning frameworks, enabling the production of feature-level attributions and human-readable visualizations. These advancements have the potential to increase clinician trust and support error analysis, ultimately bridging the gap between performance and interpretability. Noteworthy papers include:

• Bridging Accuracy and Interpretability: Deep Learning with XAI for Breast Cancer Detection, which presents an interpretable deep learning framework for breast cancer detection achieving state-of-the-art classification performance.
• Unlocking Biomedical Insights: Hierarchical Attention Networks for High-Dimensional Data Interpretation, which introduces a novel architecture that unifies multi-level attention mechanisms and explanation-driven loss functions to deliver interpretable analysis of complex biomedical data.