Use of Artificial Intelligence for the Early Detection and Prediction of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and is a leading cause of dementia. Early detection of disease pathology and prediction of its progression from mild cognitive impairment to AD dementia has important implications in clinical practice and care planning. Current diagnostic methods largely depend on post-symptomatic clinical evaluations or invasive biomarker testing which restricts widespread adoption. This review explores six primary studies utilizing artificial intelligence techniques, specifically machine learning and deep learning algorithms trained on multimodal datasets. Studies were identified through a search of the PubMed database using outlined Medical Subject Headings (MeSH) and text-word terms, and filtered for clinical trials or randomized controlled trials. Models integrating neuroimaging, CSF biomarkers, neuropsychological assessments, and physiological signals achieved high diagnostic and predictive accuracies, surpassing traditional statistical methods. Interpretability tools improved transparency and bridged the gap between model outputs and known AD pathophysiological mechanisms. Despite significant advancements, limitations including small sample sizes, geographical and ethnic homogeneity, limited external validation, and lack of longitudinal validation persist. Future research should aim to use large-scale, multi-center, longitudinal trials to increase generalizability and build clinical trust before widespread adoption. Overall, AI shows strong potential and promise as a clinical decision support tool for early detection and predictive modeling of Alzheimer's disease.