AI-Powered Paradigm Shift

Abstract

Recent alarming data and projections have elevated Alzheimer's disease (AD) to the status of a silent pandemic. Researchers are now focusing on less invasive treatments due to the drawbacks of invasive methods and the ineffectiveness of clinical trials. This highlights the increasing importance of developing non-invasive methods for the early diagnosis of AD. However, computerized analysis of the massive amounts of data produced presents obstacles that might impede progress toward an early AD diagnosis. Therefore, to overcome these obstacles, combining AI with deep learning is essential. To usher in a new era of non-invasive medicine, this work seeks to investigate the present state of these methods for Alzheimer's disease diagnosis, with the hope of harnessing the power of these tools and using the mountain of non-invasive data that exists. The purpose of this study is to compile all published reports on AI-based Alzheimer's disease detection over the past few years.