Artificial Intelligence and Mental Well Being: A Narrative Review
Keywords:
artificial intelligence, mental well-being, conversational agents, digital mental health, large language models, digital phenotyping, chatbot therapy.Abstract
The global burden of mental illness continues to outpace the capacity of health systems to respond to it. According to the latest Global Burden of Disease estimates published in 2026, approximately 1.17 billion people worldwide were living with a mental disorder, highlighting the growing global burden and persistent gaps in access to adequate mental health care [1].In this context, artificial intelligence (AI) is no longer confined to research settings — it now plays an active role in mental health service delivery, including chatbots, digital phenotyping methods, and clinical decision-support systems. This review synthesises evidence from randomised controlled trials, systematic reviews, and meta-analyses published mainly between 2017 and 2026 to assess what AI can and cannot currently deliver for mental well-being. The evidence indicates that AI-based conversational agents produce a moderate, statistically reliable reduction in depressive and anxiety symptoms, with pooled effect sizes in the range of Hedges g = 0.6–0.7 across several meta-analyses, but weaker and less consistent effects on broader psychological well-being. At the same time, a fast-growing body of work documents genuine risks: hallucinated or unsafe responses in crisis situations, algorithmic bias, data privacy exposure, and emotional dependence that can, in some users, deepen rather than relieve loneliness. The review concludes that AI is best understood not as a replacement for professional mental health care but as a conditionally effective adjunct, whose benefits depend heavily on responsible design, transparent limitations, and continued human oversight.
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