A Review on Usage of Artificial Intelligence for Early Detection and Management of Alzheimer's Disease

Abstract

Artificial Intelligence (AI) has emerged as a powerful tool in Alzheimer's disease (AD) research and clinical practice. This review discusses about the recent advances in AI applications for AD, focusing on neuroimaging analysis, biomarker discovery, cognitive assessment, and predictive modeling. AI techniques, particularly deep learning algorithms, have significantly improved the accuracy and efficiency of brain imaging interpretation, enabling earlier detection of AD-related structural and functional changes. In biomarker research, AI has accelerated the identification of novel blood-based and CSF markers, potentially leading to less invasive and more cost-effective diagnostic methods. AI-driven cognitive assessment tools, including computerized tests and speech analysis, offer more sensitive measures of cognitive decline. Additionally, AI-based predictive models integrating multiple data types show promise in personalized risk assessment and disease progression forecasting. Despite these advancements, challenges remain in data standardization, model interpretability, and ethical considerations. This review explains about the current state of AI in AD research, its potential impact on patient care, and areas requiring further investigation to fully realize the benefits of AI in combating Alzheimer's disease