The application of AI-driven digital phenotyping and interventions in neuropsychiatric disorders

Artificial intelligence (AI) is increasingly influencing the management of neuropsychiatric disorders, extending its role beyond assistive diagnosis toward adaptive intervention and individualized disease management. Digital phenotyping enables continuous assessment of multimodal behavioral and biological signals, allowing neuropsychiatric disorders to be modeled as dynamic rather than static processes. This review summarizes current AI-driven approaches for neuropsychiatric assessment and intervention, including computer vision-based behavioral analysis, wearable sensing, neuroimaging biomarkers, digital therapeutics, virtual reality, social robotics, and brain-computer interfaces. We further discuss emerging closed-loop frameworks that integrate real-time state estimation with adaptive intervention strategies, shifting neuropsychiatric AI from passive observation toward continuous therapeutic optimization. Despite rapid progress, integration between phenotyping and intervention remains limited, and most closed-loop systems are still confined to experimental settings. Key challenges include multimodal representation, interpretability, generalizability across populations and devices, and translation into real-world clinical workflows. We argue that the primary bottleneck in AI-driven neuropsychiatry is no longer prediction accuracy alone, but the ability to connect continuous patient-state estimation with clinically meaningful adaptive action. Finally, we outline future directions toward clinically grounded, human-AI collaborative neuropsychiatric systems.