Abstract
Alzheimer's disease (AD), the leading cause of dementia, poses a growing global health challenge due to an aging population. Early and accurate diagnosis is essential for optimizing treatment and management, yet traditional diagnostic methods often fall short in addressing the complexity of AD pathology. Recent advancements in radiomics and artificial intelligence (AI) offer novel solutions by integrating quantitative imaging features and machine learning algorithms to enhance diagnostic and prognostic precision. This review explores the application of radiomics and AI in AD, focusing on key imaging modalities such as PET and MRI, as well as multimodal approaches combining structural and functional data. We discuss the potential of these technologies to identify disease-specific biomarkers, predict disease progression, and guide personalized interventions. Additionally, the review addresses critical challenges, including data standardization, model interpretability, and the integration of AI into clinical workflows. By highlighting current achievements and identifying future directions, this article underscores the transformative potential of AI-driven radiomics in reshaping AD diagnostics and care.