Overview of Artificial Intelligence Methods for Alzheimer's Disease Prediction and Progression

Years of dedicated experimental and clinical investigations have shed light on numerous aspects of Alzheimer's disease (AD) pathogenesis. However, the are still many facets and issues unsolved. Recent advancements in open data-sharing initiatives, which gather data encompassing lifestyle, clinical records, and biological information from individuals with AD, have presented an almost inexhaustible wealth of data related to the disease. This data surpasses human capacity to comprehensively analyze. Additionally, the integration of extensive Big Data derived from multi-omics studies holds the promise of delving into the pathophysiological mechanisms spanning the entire biological spectrum of AD. In this context, Artificial Intelligence (AI) techniques can give a valid support to public health authorities, complementing traditional approaches with advanced tools. This study provides a focused review of methods, algorithms, applications, and emerging AI technologies that can be utilized for forecasting and diagnosing Alzheimer's disease. The main objectives of this review are understanding the importance of AI approaches such as machine learning and deep learning for AD, and discussing the efficiency and impact of these methods for AD prediction and progression.