Alzheimer’s disease (AD) causes progressive cognitive decline, where early detection is critical for effective intervention. While deep learning models have achieved high detection accuracy in AD diagnosis, their lack of interpretability has led to skepticism among medical professionals. This paper introduces a novel pre-model approach leveraging Jacobian Maps (JM) within a multi-modal framework to improve interpretability and trustworthiness in AD detection. By capturing localized brain volume changes, JMs enhance model explainability by correlating predictions with established neuroanatomical biomarkers of AD. We validate the effectiveness of JMs through experiments comparing the performance of a 3D CNN trained on JMs versus traditional preprocessed data, which demonstrates superior accuracy. Additionally, we provide both visual and quantitative insights using 3D Grad-CAM analysis, demonstrating improved interpretability and diagnostic accuracy.