The use of artificial intelligence methods in the field of medicine has become widespread, helping to diagnose, analyze and make recommendations for treatment. Psychiatry is a branch of medicine that studies mental disorders, methods for their diagnosis and treatment. Her range of tasks includes not only diagnosis and treatment, but also observation, monitoring and subsequent rehabilitation of patients. This subject area has significant problems, such as objectivity, inconsistency in the diagnosis, the complexity of the classification of diseases, and the unpredictability of the course of the disease. With a number of these problems, the use of machine learning methods and artificial intelligence algorithms helps to cope. This paper is devoted to a review of research on artificial intelligence methods used to solve problems in the field of psychiatry. The relevance of the topic is due to the high need for improvements in this subject area. Specific issues are presented in this article. Among them, the main directions were identified: data deidentification, classification of symptom severity, accuracy of condition prediction. To solve them, the authors used such methods as latent semantic analysis for natural language processing, classification methods, convolutional neural networks for prediction, and cognitive modeling. Separately, the effectiveness of hybrid systems, including the implementation of several machine learning methods at once, is noted. The aim of the study was to highlight the main directions of development of research in the scientific community, which demonstrate the successful integration of artificial intelligence into psychiatry, as well as to compare them with each other according to the obtained estimates of the accuracy of the models. Which, in turn, implies the analysis and analysis of specific algorithms, their performance for specific tasks
