This study by Humayan et al. analyzes how artificial intelligence (AI)-driven tools like conversational agents can improve mental health outcomes by reducing symptoms of depression and anxiety across diverse populations. The authors conducted a review of peer-reviewed studies published between January 2000 and July 2024 on conversational agents for mental health, searching multiple databases (including PubMed, Elsevier, Scopus, and Google Scholar), and including research articles with measurable outcomes for depression or anxiety. Using a random-effects meta-analysis, the researchers then combined and analyzed results from the studies to calculate the overall effectiveness of AI-based mental health tools across populations. The meta-analysis found that AI-based conversational agents significantly reduce both anxiety and depression. Furthermore, multimodal conversational agents (such as those using voice, text, or other input types) showed larger effect sizes than text-only systems. However, long-term effects were inconsistent, as many studies had short follow-up periods, and differences in populations, interventions, and measurements limited generalisability. The authors argue that AI-driven conversational agents may offer a scalable and easily accessible complement to traditional mental health care, especially for reducing depression and anxiety in the short term. They do note that for broader implementation, studies must address long-term effectiveness, standardise methodologies, account for algorithmic bias and privacy issues, and ensure intervention equity across populations.