Alzheimer's disease (AD) is a globally rising health concern and problem. It is a progressive neurodegenerative disorder and leading cause of dementia, with rising prevalence and no direct cure. Current diagnostic methods often detect AD only after significant neuronal damage, limiting opportunities for early intervention. Due to the lack of effective diagnostic methods, new research is being developed for innovative solutions. Artificial intelligence (AI) offers promising solutions by applying machine learning and deep learning to neuroimaging modalities such as MRI and PET, enabling detection of subtle brain changes years before clinical symptoms emerge. This paper reviews AD's pathogenesis, clinical heterogeneity, traditional diagnosis methods (and their limitations), AI's emergence in the neuroimaging field, and then evaluates three emerging AI-based models: CAPCBAM, D3LM-LAN, and MLM-MCSVM. Among these, CAPCBAM demonstrates the highest performance, achieving near-perfect accuracy (~99–100%) while preserving spatial hierarchies and improving interpretability through attention mechanisms. D3LM-LAN and MLM-MCSVM also show strong results in multimodal classification and early-stage detection, respectively. Despite their promise, challenges such as computational expenses, dataset bias, and limited clinical integration remain. These can be addressed through computational optimization, a diversified, varying dataset, and expanding the scope of both representatives of different regions. In regards to clinician relationships, AI can serve as a "decision-report" system that can speed up traditional diagnoses while clinicians focus on patient relationships. Overall, this research will hopefully provide insight into this developing field, foster further innovations, and truly integrate AI into clinical workspaces.
J. Chen (2025) studied this question.