{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T23:20:16Z","timestamp":1792452016563,"version":"4.5.0"},"reference-count":54,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The increasing prevalence of mental health issues among adolescents and young adults, coupled with barriers to accessing traditional therapy, has led to growing interest in artificial intelligence (AI)-driven conversational agents (CAs) as a novel digital mental health intervention. Despite accumulating evidence suggesting the effectiveness of AI-driven CAs for mental health, there is still limited evidence on their effectiveness for different mental health conditions in adolescents and young adults.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to examine the effectiveness of AI-driven CAs for mental health among young people, and explore the potential moderators of efficacy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 5 main databases (PubMed, PsycINFO, Embase, Cochrane Library, and Web of Science) were searched systematically dated from the establishment of the database to August 6, 2024. Randomized controlled trials comparing AI-driven CAs with any other type of control condition in improving depressive symptoms, generalized anxiety symptoms, stress, mental well-being, and positive and negative affect were considered eligible when they were conducted in young people aged 12-25 years. The quality of these studies was assessed using the Cochrane Risk of Bias tool. Data were extracted by 2 independent reviewers and checked by a third reviewer. Pooled effect sizes (Hedges g) were calculated using random effect models and visually presented in forest plots.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 14 articles (including 15 trials) were included, involving 1974 participants. The results indicated that, after adjustment for publication bias, AI-driven CAs had a moderate-to-large (Hedges g=0.61, 95% CI 0.35-0.86) effect on depressive symptoms compared to control conditions. However, their effect sizes adjusting for publication bias for generalized anxiety symptoms (Hedges g=0.06, 95% CI \u20130.21 to 0.32), stress (Hedges g=0.002, 95% CI \u20130.19 to 0.20), positive affect (Hedges g=0.01, 95% CI \u20130.24 to 0.27), negative affect (Hedges g=0.07, 95% CI \u20130.13 to 0.27), and mental well-being (Hedges g=0.04, 95% CI \u20130.21 to 0.29) were all nonsignificant. Subgroup analyses revealed that AI-driven CAs were particularly effective in improving depressive symptoms among subclinical populations (Hedges g=0.74, 95% CI 0.50-0.98).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The findings highlight the potential of AI-driven CAs for early intervention in depression among this population, and underscore the need for further improvements to enhance their efficacy across a broader range of mental health outcomes. Key limitations of the reviewed evidence include heterogeneity in therapeutic orientations of CAs and lack of follow-up measures. Future research should explore the long-term effects of AI-driven CAs on mental health outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/69639","type":"journal-article","created":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T09:55:17Z","timestamp":1743242117000},"page":"e69639","source":"Crossref","is-referenced-by-count":79,"title":["Effectiveness of AI-Driven Conversational Agents in Improving Mental Health Among Young People: Systematic Review and 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