A Review of the Use of Artificial Intelligence Models for Predicting the Treatment Progression and Outcomes of Patients with Psychiatric Disorders

The integration of Artificial Intelligence (AI) into psychiatry offers a promising solution for predicting treatment outcomes in patients with mental health disorders. This scoping review explores the application of AI models, particularly machine learning (ML) and deep learning techniques, in forecasting treatment responses for psychiatric conditions such as depression, anxiety, and schizophrenia. AI models have demonstrated significant potential in personalizing treatment plans by analyzing diverse datasets, including clinical records, genetic data, neuroimaging results, and behavioral patterns. These models have shown efficacy in predicting the effectiveness of medications, such as antidepressants and antipsychotics, as well as therapeutic interventions like cognitive behavioral therapy (CBT). However, challenges persist, including the variability in model performance due to biases in data, limited representation of diverse populations, and the complexity of psychiatric disorders. Moreover, the lack of standardized data collection methods and the need for continuous validation remain critical barriers to the widespread adoption of AI in clinical settings. Despite these limitations, AI’s ability to provide real-time, data-driven insights and assist in decision-making offers substantial improvements in treatment accuracy and efficiency. This review highlights the need for further research focused on improving data quality, addressing biases, and developing standardized methodologies to enhance the integration of AI into psychiatric care. The findings underscore the potential of AI to revolutionize mental health treatment and provide more personalized, effective care for patients.

کلیدواژه ها:

Artificial Intelligence (AI) ، Psychiatric Disorders ، Treatment Prediction ، Machine Learning