The use of artificial intelligence (AI) is one of the most important issues and challenges in contemporary psychiatry. Its growing relevance is driven by the increasing availability of clinical, neurobiological, and digital data, as well as by the limitations of traditional diagnostic approaches based primarily on clinical assessment. In recent years, AI has been applied in several areas of psychiatry, including the diagnosis of mental disorders, suicide risk assessment, prediction of relapse and hospitalization, treatment personalization, natural language analysis, and patient monitoring using mobile and wearable technologies. Key approaches include machine learning, deep learning, natural language processing, and digital phenotyping, which enable the analysis of complex and multidimensional datasets. AI may support the identification of patterns that are difficult to capture using conventional clinical assessment, thereby contributing to earlier detection of mental disorders, improved risk stratification, and better personalization of therapeutic interventions. Clinical decision support systems, digital monitoring tools, and therapeutic chatbots based on generative AI are also becoming increasingly important. Despite promising research findings, the implementation of AI in routine clinical practice remains limited by data heterogeneity, insufficient external validation, limited interpretability, algorithmic bias, and unclear frameworks of clinical responsibility. The development of generative AI has further intensified the debate on safety, rapid commercialization, open-source solutions, and the limits of automating the therapeutic relationship. The aim of this review is to present current applications of AI in psychiatry, assess their clinical relevance, and discuss the major methodological, ethical, and organizational challenges associated with future development.