Implementation Determinants for AI-Enabled Digital Mental Health Interventions Across Asian Mental Health Systems: A Scoping Review Using the Donabedian Model.

Mental health disorders impose a rapidly growing burden across Asia, where treatment gaps routinely exceed 70% due to fragmented services, workforce shortages, and underinvestment in mental health infrastructure. AI-enabled digital interventions are widely promoted as scalable solutions, yet implementation frequently falters at the interface between technology design and local health-system, sociocultural, and infrastructural realities. No synthesis has systematically mapped implementation determinants for these tools across Asian mental health ecosystems using a service-quality framework. This scoping review addresses that gap. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, a systematic search was conducted across five electronic databases (PubMed, Scopus, Web of Science, JMIR, and ScienceDirect) for peer-reviewed, English-language studies published between January 2015 and December 2025. Eligible studies included empirical research, reviews, and conceptual analyses focusing on barriers to AI-enabled mental health screening and assessment among Asian populations. Data were extracted from 12 included sources and synthesised using descriptive content analysis organised by the Donabedian framework (Structure, Process, Outcome), with each study coded for evidence maturity and mapped against a taxonomy of AI tool types and the mental health care continuum. Structural barriers included limited organisational readiness, absent governance frameworks, and inadequate digital infrastructure. Process-level barriers arose from cultural and contextual mismatch between Western-centric tool designs and Asian collectivist values, language difficulties, suboptimal user-experience designs, and concerns about clinical safety of AI outputs. Outcome-level barriers manifested as low perceived relevance, poor user engagement, and reduced utilisation driven by concerns over privacy, accuracy, and the absence of human-like emotional support. Evidence concentrated on chatbots and screening tools at prevention and help-seeking stages, with critical gaps in governance, clinical-workflow integration, crisis response, and real-world service evaluation. Implementation success depends on governance and safety infrastructure, culturally valid and linguistically appropriate design, workforce integration and supervision, and explicit equity safeguards. A system-readiness checklist is proposed to guide planners and mental health service leaders in embedding AI tools within care pathways that preserve meaningful human involvement in clinical decision-making.