  Alzheimer's Disease (AD) is a severe brain disorder, destroying memories and
brain functions. AD causes chronically, progressively, and irreversibly
cognitive declination and brain damages. The reliable and effective evaluation
of early dementia has become essential research with medical imaging
technologies and computer-aided algorithms. This trend has moved to modern
Artificial Intelligence (AI) technologies motivated by deeplearning success in
image classification and natural language processing. The purpose of this
review is to provide an overview of the latest research involving deep-learning
algorithms in evaluating the process of dementia, diagnosing the early stage of
AD, and discussing an outlook for this research. This review introduces various
applications of modern AI algorithms in AD diagnosis, including Convolutional
Neural Network (CNN), Recurrent Neural Network (RNN), Automatic Image
Segmentation, Autoencoder, Graph CNN (GCN), Ensemble Learning, and Transfer
Learning. The advantages and disadvantages of the proposed methods and their
performance are discussed. The conclusion section summarizes the primary
contributions and medical imaging preprocessing techniques applied in the
reviewed research. Finally, we discuss the limitations and future outlooks.
