Multimodal approaches in precision psychiatry

Precision psychiatry is an emerging framework that seeks to improve diagnosis, prognosis, and treatment in mental disorders through the integration of biological, behavioral, environmental, and clinical information. Current diagnostic systems show substantial heterogeneity within categories and symptom overlap across disorders, reflecting limited biological validity and constraining treatment development. This review presents a conceptual framework for precision psychiatry that emphasizes multimodal approaches. We describe key sources of information, including demographic and psychosocial factors, as well as biological data, particularly neuroimaging, genetics, and molecular profiling. We also outline analytic strategies designed to move beyond showing average associations, including mediation and interaction analyses and machine learning approaches that enable the evaluation of intermediate pathways and heterogeneous effects. Despite increasing availability of multimodal data, key challenges include limited clinical utility of biomarkers when used in isolation and insufficient availability of integrated datasets. Shared infrastructures and validated analytic platforms are essential to support multimodal approaches and translation into clinical care.