AI in schizophrenia treatment: personalizing therapy and predicting relapses.

Abstract

The objective of this study is to present the potential of Artificial Intelligence (AI) in the treatment of schizophrenia, with a particular focus on therapy personalization and relapse prediction. The significance of schizophrenia as a major health issue and the associated therapeutic challenges, such as the low effectiveness of traditional treatment methods and difficulties in patient adherence to recommendations, are discussed. The study analyzes the application of AI in processing large datasets, including biometric, behavioral, and genetic information, which support the individualization of therapy. Examples of AI-driven projects are presented, such as patient language analysis for early relapse detection and real-time health monitoring devices. It is highlighted that such solutions can improve treatment effectiveness and reduce hospitalization rates. Special attention is given to predictive algorithms based on machine learning that enable the identification of schizophrenia relapses through complex data patterns. The study also emphasizes the integration of AI with VR (Virtual Reality) and AR (Augmented Reality) technologies, which can help patients manage their symptoms more effectively. The paper concludes with reflections on the future of AI in psychiatry, pointing to its potential to improve the accessibility and quality of healthcare, particularly in resource-limited regions.