Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β plaques and tau neurofibrillary tangles, necessitating early and accurate diagnosis for effective intervention. Recent advances in Artificial Intelligence (AI) have significantly enhanced the discovery and interpretation of AD biomarkers by enabling the integration of complex, multimodal datasets, including neuroimaging, molecular, genetic, and digital biomarkers. AI-driven approaches, particularly machine learning and deep learning, have demonstrated high accuracy in identifying early pathological changes, predicting disease progression, and distinguishing AD from mild cognitive impairment. Emerging digital biomarkers derived from speech, motor activity, cognitive tests, and wearable sensors further expand non-invasive diagnostic capabilities. Despite promising results, challenges such as data heterogeneity, lack of external validation, limited interpretability (“black-box” models), and difficulties in clinical translation remain substantial barriers. Future research is expected to focus on multimodal data fusion, explainable AI, longitudinal modelling, and personalized prediction frameworks, along with improved standardization and regulatory validation. AI-based biomarker research holds potential to transform early diagnosis, prognosis, and precision medicine in Alzheimer’s disease.
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Keywords
Alzheimer’s Disease
Artificial Intelligence
Biomarkers
Deep learning
Early diagnosis
Machine learning
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Abbreviations
AD: Alzheimer's Disease
AI: Artificial Intelligence
ML: Machine Learning
DL: Deep Learning
MRI: Magnetic Resonance Imaging
PET: Positron Emission Tomography
CSF: Cerebrospinal Fluid
MCI: Mild Cognitive Impairment
fMRI: Functional Magnetic Resonance Imaging
CNNs: Convolutional Neural Networks
RNNs: Recurrent Neural Networks
XAI: Explainable Artificial Intelligence.