Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection

The integration of multimodal data has emerged as a powerful strategy for enhancing the accuracy and interpretability of artificial intelligence (AI) models in the diagnosis and prognosis of Alzheimer's Disease (AD). This systematic review presents a comprehensive synthesis of recent advances in AI-driven multimodal fusion approaches for AD prediction. A detailed examination of widely used datasets-including their modalities, preprocessing pipelines, and accessibility-is provided to aid reproducibility and methodological transparency. We analyze and categorize the various data harmonization and preprocessing techniques employed across neuroimaging (e.g., fMRI, sMRI, PET), electrophysiological (EEG), and genomic modalities, highlighting domain-specific practices and challenges. Furthermore, fusion strategies are classified into data-level, feature-level, decision-level, and temporal (early, intermediate, and late) paradigms, offering insights into their implementation and diagnostic impact. The review also investigates the adoption of explainable AI (XAI) techniques across studies and identifies a significant underrepresentation of works that simultaneously emphasize multimodality, explainability, and methodological rigor. By adhering to both PRISMA and Kitchenham's guidelines, this review ensures transparency and replicability in evidence synthesis. Compared to existing reviews, our work uniquely focuses on the intersection of multimodal integration and explainability within a systematically validated framework. The review concludes with recommendations for future research aimed at developing robust, interpretable, and clinically relevant AI models for AD.