Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence

Alzheimer's disease (AD) is a multidimensional, complex condition that affects individuals all over the world. Despite decades of experimental and clinical research that has revealed various processes, many concerns concerning the origin of Alzheimer's disease remain unresolved. Despite the notion that there isn't a complete set of jigsaw pieces, the growing number of public data-sharing initiatives that collect biological, clinical, and lifestyle data from those suffering from Alzheimer's disease has resulted in virtually endless volumes of knowledge about the disorder, far beyond what humans can comprehend. Furthermore, combining Big Data from multi-omics research gives a chance to investigate the pathophysiological processes underlying the whole biological spectrum of Alzheimer's disease. To improve knowledge on the subject of Alzheimer's disease, Artificial Intelligence (AI) offers a wide variety of approaches for evaluating complex and significant data. The introduction of next-generation sequencing and microarray technologies has resulted in significant growth in genetic data research. When it comes to assessing such complex projects, AI technology beats conventional statistical techniques of data processing. This review focuses on current research and potential challenges for AI in Alzheimer's disease research. This article, in particular, examines how AI may assist healthcare practitioners with patient stratification, estimating an individual's chance of AD conversion, and diagnosing AD using computer-aided diagnostic methodologies. Ultimately, scientists want to develop individualized, efficient medicines.