Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being

Abstract

Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage, there is a scarcity of comprehensive evaluations of their impact on mental health and well-being. This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health and factors influencing their effectiveness and user experience. Twelve databases were searched for experimental studies of AI-based CAs' effects on mental illnesses and psychological well-being published before May 26, 2023. Out of 7834 records, 35 eligible studies were identified for systematic review, out of which 15 randomized controlled trials were included for meta-analysis. The meta-analysis revealed that AI-based CAs significantly reduce symptoms of depression (Hedge's g 0.64 [95% CI 0.17-1.12]) and distress (Hedge's g 0.7 [95% CI 0.18-1.22]). These effects were more pronounced in CAs that are multimodal, generative AI-based, integrated with mobile/instant messaging apps, and targeting clinical/subclinical and elderly populations. However, CA-based interventions showed no significant improvement in overall psychological well-being (Hedge's g 0.32 [95% CI -0.13 to 0.78]). User experience with AI-based CAs was largely shaped by the quality of human-AI therapeutic relationships, content engagement, and effective communication. These findings underscore the potential of AI-based CAs in addressing mental health issues. Future research should investigate the underlying mechanisms of their effectiveness, assess long-term effects across various mental health outcomes, and evaluate the safe integration of large language models (LLMs) in mental health care.

Fig. 1. PRISMA flow diagram.

Search and study selection process.

Fig. 2. Summary of psychological outcomes evaluated in the studies.

A total of 14 distinct psychological outcomes were evaluated in the 35 studies. The color of the bar denotes study type (Quasi-experiment or RCT). The number displayed on each bar represents the number of studies that evaluated the specific outcome within the given study type.

Fig. 3. Effects of AI-based CA interventions on psychological distress.

Note: the pooled effect sizes (Hedges’g) on psychological distress were reverse coded from their original values to align with the directionality of the pooled effect sizes on psychological well-being, i.e., positive effect sizes indicate a more favorable outcome for the CA intervention compared to control conditions.

Fig. 4. Effects of AI-based CA interventions on psychological well-being.

Note: positive effect sizes indicate a more favorable outcome for the CA intervention compared to control conditions.