Digital intelligence for depression governance: an environome framework to bridge research and policy

Background Digital intelligence platforms are transforming how policymakers synthesize and translate mental health evidence into actionable strategies. Depression research is vast but highly fragmented, limiting its effective use in policy design. We introduce the Interactive Depression Dashboard (InDepDash), designed to integrate and operationalize multidisciplinary evidence on depression. Methods InDepDash aggregates and analyzes 139,761 depression-related studies (from 2005 to 2025) spanning clinical, social, and environmental domains. The platform maps evidence across multiple determinants of mental health, conceptualized as the “environome,” including personal, social, natural, and built factors. Ontology-based annotation, semantic search, and co-occurrence analysis enable real-time exploration of research trends, gaps, and priorities, while case study applications illustrate its policy relevance. Results The platform identified substantial imbalances in the evidence base. Natural and built environmental determinants represented 14.3% of the analyzed corpus; within this domain, climate-change-specific evidence was comparatively sparse. InDepDash effectively highlights underexplored domains and supports prioritization of interventions, including neuroinclusive policies, disaster resilience strategies, and climate-informed mental health planning. Conclusion Evidence-mapping platforms such as InDepDash provide critical infrastructure for adaptive and evidence-informed governance in mental health. By enhancing the accessibility, transparency, and integration of complex data, they enable policymakers to respond to emerging challenges with greater speed. This study supports the Trustworthiness, Reproducibility, Explainability, Applicability, and Transparency (TREAT) framework as a foundation for ethical AI deployment, addressing key gaps in research translation while working toward global mental health equity.