Purpose This study aims to understand how the use of artificial intelligence (AI)-based platforms could benefit the public mental health systems, especially in low- and middle-income countries, where access and delivery issues are pervasive. Design/methodology/approach A systematic literature review of peer-reviewed studies published between 2018 and 2025 was conducted across major databases. In total, 43 papers were analysed using a mixed-methods (deductive/inductive) approach, which identified themes of platform capability, adoption and scaling determinants and governance and equity risks. Findings Although AI makes cooperation and decision-making easier for the public mental health-care system, its success depends on infrastructure, workforce readiness and data interoperability. This study identifies low interoperability, poor governance and digital divides as obstacles and proposes a four-stage, context, constraint-sensitive adoption process. Research limitations/implications This review is based on published scholarly works and does not include recent developments of the current calendar year 2026, particularly in digital health care. Further studies should focus on testing the developed theoretical framework in real-world conditions. Practical implications The results underscore the importance of devising contextually appropriate implementation that focuses on building infrastructure, digital literacy and mixed service delivery. Originality/value This study contributes to the literature by shifting the emphasis to a context-dependent, systems-based approach. This research proposes a four-phase AI adoption process that incorporates interoperability, equity and governance.
Shalender et al. (Sun,) studied this question.