Transforming depression research: The role of artificial intelligence and computational modelling

Depression is one of the leading causes of disability worldwide, affecting more than 300 million people and presenting significant challenges due to its multifactorial pathophysiology, clinical heterogeneity, variability in symptom presentation, and differences in treatment response. Approximately one-third of patients experience treatment-resistant depression (TRD), highlighting the limitations of currently available therapeutic approaches in a substantial subset of individuals. Recent advances in artificial intelligence (AI), machine learning (ML), and computational biology have created new opportunities to address these challenges and improve depression research and clinical decision-making. This narrative review examines the emerging role of AI and computational methodologies in improving the understanding of depression pathophysiology, diagnosis, monitoring, treatment selection, and antidepressant discovery. In addition to analyzing the current state of affairs, the review also critically assesses the advantages, disadvantages, clinical utility, and translational readiness of existing computational models by focusing on issues related to data quality, external validation, transparency, reproducibility, ethics, and regulation. Current evidence suggests that AI and computational approaches have considerable potential to advance precision psychiatry and therapeutic innovation in depression. However, rigorous validation, transparency, and successful clinical implementation remain essential prerequisites for their widespread adoption.