Towards Explainable AI: A Framework for Interpretable Deep Learning in High-Stakes Domains

As deep learning models are increasingly used in high-stakes fields such as healthcare, the demand for interpretable and reliable artificial intelligence has grown. Although deep neural networks achieve state-of-the-art accuracy in complex pattern recognition tasks, their opaque "black-box" approach restricts transparency and trust, particularly in critical areas such as medical diagnosis. This paper presents a unified architecture for interpretable deep learning, integrating prediction and interpretation pipelines to certify that the AI model’s decisions are not only accurate but also explainable. The classification of Alzheimer's disease was chosen as the case study of this paper, and four Convolutional Neural Network models were analyzed: Custom CNN, MobileNetV2, ResNet50, and EfficientNetB0. These models are both trained and validated using the available dataset with brain MRI pictures. MobileNetV2 and ResNet50, when used without pretrained weights, failed to generalize due to their inability to meet the localization requirement; thus, indicating the need for architectural tweaking and domain alignment. The architecture recommended incorporates advancements such as Grad-CAM visualizations, Layer-Wise Relevance Propagation, and attention mechanisms, enabling an enhanced understanding of classified data. The domain-customization layer then synthesizes these complex findings into clinically interpretable results, narrowing the gap between AI models and medical professionals. Thus, the suggested architecture not only improves diagnostic dependability but also increases transparency, which fosters trust and compliance with ethical principles for the users.