Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.

ABSTRACT: Alzheimer's disease (AD) remains a significant global health challenge, affecting millions worldwide and imposing substantial burdens on healthcare systems. Advances in artificial intelligence (AI), particularly in deep learning and machine learning, have revolutionized neuroimaging-based AD diagnosis. However, the complexity and lack of interpretability of these models limit their clinical applicability. Explainable Artificial Intelligence (XAI) addresses this challenge by providing insights into model decision-making, enhancing transparency, and fostering trust in AI-driven diagnostics. This review explores the role of XAI in AD neuroimaging, highlighting key techniques such as SHAP, LIME, Grad-CAM, and Layer-wise Relevance Propagation (LRP). We examine their applications in identifying

SUBMITTER: Taiyeb Khosroshahi M 

PROVIDER: S-EPMC11899653 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature