Responsible and innovative AI for mental health care: five priority themes

Résumé fourni par la source

Artificial intelligence (AI) has entered psychiatry at scale, yet its clinical impact remains constrained by a sizable gap between technical validation and real-world implementation. The central barriers are no longer computational, but infrastructural: unreliable measurement systems, incomplete governance frameworks, and insufficient standards for clinical evidence and integration. This paper synthesizes insights from a 2026 American College of Neuropsychopharmacology (ACNP) study group examining how to responsibly translate AI into clinical mental health care. Building on this perspective, we outline five priorities required for clinical impact. First, robust measurement and phenotyping infrastructure, such as reliable psychometrics, digital phenotyping, and standardized data pipelines, is essential for clinically meaningful AI. Second, the most immediate and scalable impact of AI lies in clinician-facing augmentation tools that reduce workflow burden, such as ambient documentation systems and structured decision-support pipelines, with important research to conduct here. Third, patient-facing AI interventions show promise but require rigorous safety evaluation, particularly for implicit suicide risk and heterogeneous treatment effects. Fourth, governance and equity frameworks must extend beyond privacy to address bias, digital literacy, and research integrity. Fifth, future progress requires moving beyond predictive models toward causal, mechanistic approaches to precision psychiatry that better inform treatment decisions and clinical action. Together, these priorities define a translational agenda for 2026 and beyond: AI in mental health will succeed not through model performance alone, but through disciplined integration into clinical workflows, measurement systems, and governance structures that ensure safety, equity, and real-world effectiveness.

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