Artificial intelligence and major depression: Toward mechanistic and clinically actionable models

Abstract

Major depressive disorder is a widespread psychiatric disorder driven by complex genetic, neurobiological, psychological, and environmental mechanisms. Conventional diagnostic systems, such as the Diagnostic and Statistical Manual of Mental Disorders, the Fifth Edition and International Classification of Diseases, rely on symptom-based evaluations, which are limited by subjectivity, symptom overlap, and restricted applicability across diverse populations. Advances in artificial intelligence (AI) provide new opportunities for objective, data-driven depression assessment. This review synthesizes epidemiological, etiological, and clinical evidence to evaluate AI-based approaches for depression detection and characterization. Machine learning, deep learning, and large language model-based methods applied to multimodal data, including electronic health records, neuroimaging, electroencephalography (EEG), speech and language data, and digital behavioral signals, were systematically examined, with particular attention to interpretability and ethical considerations. Depression was consistently associated with monoaminergic and neurotrophic dysregulation, inflammation, hypothalamic pituitary adrenal axis dysfunction, and frontolimbic network abnormalities. AI models demonstrated strong discriminative performance using biological and behavioral markers, particularly when multimodal data integration was employed. Neuroimaging and EEG analyses revealed network-level alterations, while natural language processing approaches captured linguistic and acoustic markers linked to symptom severity and suicide risk. AI-based systems have substantial potential to advance precision psychiatry by enabling earlier detection and personalized treatment of depression. However, challenges, including dataset bias, methodological heterogeneity, limited interpretability, and insufficient real-world validation, must be addressed through standardized, transparent, and ethically guided clinical research.