The application progress of artificial intelligence in the diagnosis of Alzheimer’s disease
- 1Liaoning University of Traditional Chinese Medicine , ,
- 2Liaoning University of Traditional Chinese Medicine Affiliated Hospital , ,
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.
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BibTeX
@article{liu2026application,
title = {{The application progress of artificial intelligence in the diagnosis of Alzheimer’s disease}},
author = {Enqi Liu and Meijia Cheng and Yetao Ju and Yi Ru and Yongming Liu and Dongyu Min},
journal = {Frontiers in neurology},
year = {2026},
month = oct,
doi = {10.3389/fneur.2026.1852128},
url = {https://europepmc.org/article/PMC/PMC13669838},
note = {CC BY},
}
RIS
TY - JOUR
TI - The application progress of artificial intelligence in the diagnosis of Alzheimer’s disease
AU - Enqi Liu
AU - Meijia Cheng
AU - Yetao Ju
AU - Yi Ru
AU - Yongming Liu
AU - Dongyu Min
PY - 2026
DA - 2026/10/01
JO - Frontiers in neurology
DO - 10.3389/fneur.2026.1852128
UR - https://europepmc.org/article/PMC/PMC13669838
AB - 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.
SN - 1664-2295
ER -