AI and Neural Network Models For Personalized Mental Health Interventions

Abstract

Advances in artificial intelligence (AI) and neural network architectures have created new opportunities to deliver personalized mental health interventions that are adaptive, scalable, and sensitive to within-person dynamics. This paper synthesizes contemporary methodological advances in deep learning, multimodal fusion, and sequential modeling that enable individualized detection, prediction, and delivery of interventions in naturalistic settings. We highlight how ecological momentary assessment (EMA) and passive sensing generate high-resolution longitudinal data that, when coupled with supervised and unsupervised neural models, support momentary prediction of affective states and the design of just-in-time adaptive interventions (JITAIs). Key technical challenges include handling heterogeneity and nonstationarity in personal data streams, model interpretability and fairness, and safeguarding privacy in pervasive monitoring; we discuss contemporary methods such as transfer learning, personalized fine-tuning, attention-based time-series models, and explainable AI techniques that address these issues. Finally, we propose a unified research agenda and evaluation framework emphasizing measurement-based care, clinical validity, transparent reporting, and ethical deployment to accelerate the translation of neural-network-based personalization into safe, equitable mental health practice.