Onwards Transparent Medical AI: A Dual-XAI Framework for Explainable 3D MRI-Based Alzheimer’s Screening

The development of explainable diagnostic systems marks a critical evolution in the fight against Alzheimer’s Disease. By moving beyond "black-box" models, this research demonstrates that high-performance 3D Deep Learning can coexist with clinical transparency. The proposed framework successfully bridges the trust gap by providing neurologists with visual saliency maps and quantitative feature attributions that align with established biological pathology. Furthermore, the integration of post-hoc interpretability techniques like Grad-CAM and SHAP ensures that AI systems satisfy the stringent regulatory requirements of 2026, specifically the "Right to Explanation". Ultimately, this study transitions AI from a binary classification tool into a collaborative diagnostic aid, fostering a healthcare environment where machine precision meets human-centric reasoning to improve patient outcomes.

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