Artificial intelligence (AI) is the ability of machines to carry out tasks by imitating human intelligence. In recent years, AI methods have begun to be used in many different areas. Healthcare is one of the most common areas where AI is used. Diagnosis, treatment, patient care, new drug production, and preventive care can be listed as some of the applications of AI in health.
In this review, deep learning methods, which are a sub-branch of AI, are mentioned. Deep learning methods frequently used in the literature are convolutional neural networks, stacked autoencoders, and recurrent neural networks. The deep learning methods commonly used in the literature include convolutional neural networks (CNNs) for image recognition and classification, stacked autoencoders (SAEs) for unsupervised feature learning and dimensionality reduction, and recurrent neural networks (RNNs) for analyzing sequential data like time-series data. However, it should be noted that all these methods can be applied to other application areas as well. This paper presents studies in the literature on medical image analysis, drug discovery and development, and remote patient monitoring in which these deep learning methods are used.