JMIR Publications

ABSTRACT

Artificial intelligence (AI)-enabled health tools are increasingly promoted within healthcare policy as part of digital self-management models for mental health care. However, development is concentrated on scalable, low-intensity interventions for common conditions such as anxiety and depression, rather than on populations with the greatest clinical need, such as those with serious mental illness (SMI). This includes schizophrenia-spectrum and bipolar disorders, for example, which remain comparatively underserved, although experiencing a disproportionate burden of morbidity and service use. The clinical features of SMI -comprising fluctuating symptoms, multimorbidity, and elevated risk limit the suitability of low-intensity AI-driven self-management tools designed for mild-to-moderate conditions. This Viewpoint argues that the scarcity of AI-enabled self-management tools for SMI reflects a common issue in current innovation systems rather than one of technical infeasibility alone. Market incentives, regulatory pathways, and fragmented research pipelines favour low-risk, high-volume populations, while limiting development for clinically complex groups. Addressing this unevenness is essential and will require upstream intervention, including targeted public funding, improved data infrastructure, and administrative frameworks that support safe innovation in high-risk populations. Embedding equity for SMI as a primary design requirement will be necessary to ensure that AI-driven mental health tools do not reinforce current inequalities.