The integration of artificial intelligence (AI) into mental health and psychiatry is transforming the diagnostics and treatment of mental disorders, including major depressive disorder (MDD). While the initial and promising applications span diagnostic screening and theraputic chatbots, these first-wave technologies do not directly target the underlying brain alterations or account for the treatment of MDD. Non-invasive brain stimulation (NIBS) holds a tremendous promise to address the biological heterogeneity of MDD, but is currently hindered by highly variable outcomes. Therefore, we posit that synergistic integration of AI with NIBS represents the most promising path to address these difficulties. Importantly, the frontier for AI in depression treatment lies in a paradigm shift: from empirical trial-and-error to data-driven, personalized precision interventions. We argue for a paradigm shift away from AI roles in mental health (e.g., chatbots, diagnostics) toward its deep integration as the core engine for personalized, circuit-based neuromodulation. We highlight the key opportunities this fusion creates: identifying patient-specific neural targets through predictive modeling, developing adaptive closed-loop therapies that respond to real-time brain states, and brain "digital twins" for in silico simulation and protocol optimization. While significant challenges in data standardization, model interpretability, and clinical validation remain, the fusion of AI and NIBS heralds an era of psychiatry that is predictive, personalized, and precise.
Qiu et al. (Tue,) studied this question.