Advanced biomaterials and digital twins for precision psychiatry: neuroimmune modulation and AI-guided therapeutics

Precision psychiatry should be reframed as an integrated bioengineering problem-not simply as a computational challenge of improving diagnostic classification or treatment prediction. In this Perspective, we argue that the next stage of precision psychiatry must move beyond symptom-based stratification and algorithmic prediction toward biologically informed therapeutic engineering. We propose that advanced biomaterials should be viewed not merely as drug carriers or device components, but as programmable therapeutic interfaces capable of modulating neuroimmune and neurochemical microenvironments. Similarly, digital twins should not be treated only as predictive dashboards, but as patient-specific models that connect multimodal data with treatment selection, biomaterial design, neuromodulation parameters, and longitudinal outcome evaluation. Psychiatric disorders-including major depressive disorder, schizophrenia, bipolar disorder, anxiety disorders, obsessive-compulsive disorder, and substance use disorder-increasingly appear to involve dynamic interactions among neural circuits, immune signaling, endocrine stress responses, blood-brain barrier function, synaptic remodeling, gut-brain communication, sleep-wake rhythms, and environmental exposures. These multidimensional processes explain why patients with similar diagnoses follow different trajectories and respond differently to the same interventions. Future progress depends on three converging priorities: moving from diagnostic categorization to dynamic neuroimmune state modeling; redefining biomaterials as active modulators of psychiatric disease biology rather than passive delivery platforms; and evaluating digital psychiatry not only by predictive accuracy, but by its capacity to guide safe, mechanistically grounded, and clinically executable interventions. This framework may help reposition precision psychiatry as a translational field in which computational modeling and material-based therapeutics are developed together to support individualized mental health intervention.