Ethical implications of high attrition in AI-based mental health interventions: a systematic review and meta-analysis
Abstract
Background: The integration of AI-based conversational agents (CAs) in mental health aims to bridge the global "treatment gap." However, high user attrition poses a critical ethical challenge, potentially compromising care continuity and distributive justice. This study quantifies attrition rates in AI-driven mental health interventions and evaluates their ethical implications for user autonomy and research integrity.
Methods: Following PRISMA 2020 guidelines and registered with PROSPERO, this systematic review and meta-analysis synthesized evidence from six databases through September 2025. Employing a convergent parallel mixed-methods design, we analyzed 39 independent studies. A random-effects model was used to calculate pooled attrition rates, while meta-regression explored the correlation between attrition and clinical efficacy (improvement in depressive/anxiety symptoms). Qualitative narrative synthesis was further applied to contextualize user experiences.
Results: The meta-analysis revealed a pooled attrition rate of 17.04% (95% CI, 11.46%-23.38%) for AI interventions. Generative AI-based CAs exhibited significantly lower attrition rates (9.25%) compared to retrieval-based systems (23.12%, p = 0.044). Subgroup analysis identified intervention target, delivery platform, interaction mode, engagement reminders and safety measures as key moderators of engagement. Crucially, a significant inverse association between retention and treatment efficacy was observed in…
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