The application progress of artificial intelligence in the diagnosis of Alzheimer’s disease

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, where traditional diagnostic methods are constrained by limited sensitivity and restricted clinical utility. This review synthesizes recent applications of artificial intelligence (AI) in AD diagnostics, focusing on machine learning (ML) and deep learning (DL) across neuroimaging modalities and multi-omics research. AI algorithms can automatically extract subtle microstructural, metabolic, and molecular features, enabling high-precision classification of AD stages, patient stratification, and non-invasive early screening through multimodal data integration. However, translating AI into routine clinical practice still faces critical challenges, including high equipment costs, insufficient model generalizability across real-world cohorts, data class imbalance, and complex cross-platform standardization barriers. Future research should prioritize cost-effective surrogate modeling, robust multi-center validation, dynamic longitudinal integration, and mechanistic interpretability to accelerate the standardized clinical translation of AI-driven AD diagnostics.