PREDICTIVE VALIDITY OF AI-INTEGRATED NEUROIMAGING AND DIGITAL PHENOTYPING FOR TREATMENT SELECTION IN MAJOR DEPRESSIVE DISORDER

Major Depressive Disorder (MDD) remains a leading cause of global disability, with treatment outcomes characterized by substantial heterogeneity and limited success rates. The conventional trial-and-error approach to antidepressant selection contributes to prolonged suffering and delayed remission. This work examines the predictive validity of integrating artificial intelligence with neuroimaging and digital phenotyping to optimize treatment selection in MDD. Recent advances in machine learning have demonstrated the capacity to identify neural biomarkers, particularly pregenual anterior cingulate cortex activity, that predict treatment response across multiple interventions. Concurrently, digital phenotyping—the moment-by-moment quantification of behavior via smartphones and wearables—offers objective, continuous monitoring of mood trajectories and behavioral patterns predictive of clinical outcomes. The integration of these modalities within multimodal predictive frameworks represents a promising pathway toward precision psychiatry. However, significant challenges remain, including model generalizability, validation in diverse populations, and the translation of algorithmic predictions into clinically actionable recommendations. This work synthesizes current evidence on AI-integrated neuroimaging and digital phenotyping, evaluates their predictive validity for treatment selection, and proposes a comprehensive framework for clinical implementation.