Commercial AI-Based Mental Health Chatbots as Low-Intensity Adjuncts to Psychotherapy: Effectiveness, Adherence, and Safety - A Systematic Review and Meta-Analysis.

INTRODUCTION

The effectiveness, scalability, and safety monitoring of commercial direct-to-consumer AI mental health chatbots remain uncertain.

METHODS

In this systematic review and meta-analysis, eight databases were searched for English peer-reviewed studies (January 1, 2015-December 31, 2025). Randomized controlled trials (RCTs) were meta-analyzed with random-effects models (Hedges' g). Completion was synthesized with a one-stage binomial-logit generalized linear mixed model; attrition was pooled as the risk ratio. Safety focused on harms reporting interpretability.

RESULTS

Fifty-two studies (22 RCTs; N = 110,594) covering 13 commercial chatbots were included. Depression improved versus controls (18 RCTs; N = 3,170): g = -0.35 (95% CI: -0.56, -0.13), about 1.6 PHQ-9 points, below the 5-point minimal clinically important difference; number needed to treat about 6.6. Anxiety effects (15 RCTs; N = 2,936) were uncertain: g = -0.37 (95% CI: -0.87, 0.12). Loneliness partially reduced (4 RCTs; N = 662): g = -0.21 (95% CI: -0.39, -0.03). Completion pooled at 79.5% (95% CI: 68.0, 87.6%) but showed a scalability gradient. Attrition was similar to controls (RR = 1.01; 95% CI: 0.79, 1.29). Harms reporting was uninterpretable: only 3/22 RCTs met active-ascertainment criteria. Certainty ranged from low to very low.

CONCLUSION

Commercial AI mental health chatbots may yield modest improvements, most consistently for depression. Loneliness evidence is limited and fragile, while anxiety and affect outcomes remain heterogeneous; for anxiety, net harm cannot be excluded. Completion rates likely overestimate real-world retention, and safety cannot be inferred from "no events" claims in the absence of harms monitoring. These products are best positioned as low-intensity adjuncts requiring safety-by-design baselines.