Precision psychiatry in clinical practice: What is clinically actionable, what is promising, and what remains experimental?

Precision psychiatry seeks to improve individual-level prediction, treatment selection, monitoring, and prevention by integrating clinical, biological, behavioural, digital, and contextual information. This structured narrative review assessed the clinical readiness of major approaches using a purpose-specific framework covering validity, external replication, incremental utility, actionability, patient benefit, feasibility, equity, and governance. Approaches considered clinically actionable in selected settings included structured longitudinal assessment and decision-linked measurement-based care as precision-enabling infrastructure, therapeutic drug monitoring for selected medications and indications, and selected pharmacogenetic gene-drug interactions. Digital phenotyping, artificial intelligence-assisted decision support, neuroimaging and electrophysiological markers, peripheral biomarkers, and multimodal prediction models were classified as promising but not yet ready for routine use. Polygenic risk scores, multiomic subtyping, diagnostic neuroimaging, fully automated treatment selection, and automated prediction of suicide or acute deterioration remained predominantly experimental. Across domains, major barriers included inadequate external validation and calibration, uncertain incremental utility, limited prospective evidence of patient benefit, implementation challenges, cost, underrepresentation of diverse populations, privacy concerns, and unresolved accountability. Precision psychiatry is best understood as a dynamic translational continuum whose value should be judged by whether it supports better, safer, more equitable, and more humane clinical decisions.