Conversational AI in Pediatric Mental Health: A Narrative Review.

IF 2 4区医学 Q2 PEDIATRICS

Children-Basel Pub Date : 2025-03-14 DOI:10.3390/children12030359

Masab Mansoor, Ali Hamide, Tyler Tran

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引用次数: 0

Abstract

Background/objectives: Mental health disorders among children and adolescents represent a significant global health challenge, with approximately 50% of conditions emerging before age 14. Despite substantial investment in services, persistent barriers such as provider shortages, stigma, and accessibility issues continue to limit effective care delivery. This narrative review examines the emerging application of conversational artificial intelligence (AI) in pediatric mental health contexts, mapping the current evidence base, identifying therapeutic mechanisms, and exploring unique developmental considerations required for implementation.

Methods: We searched multiple electronic databases (PubMed/MEDLINE, PsycINFO, ACM Digital Library, IEEE Xplore, and Scopus) for literature published between January 2010 and February 2025 that addressed conversational AI applications relevant to pediatric mental health. We employed a narrative synthesis approach with thematic analysis to organize findings across technological approaches, therapeutic applications, developmental considerations, implementation contexts, and ethical frameworks.

Results: The review identified promising applications for conversational AI in pediatric mental health, particularly for common conditions like anxiety and depression, psychoeducation, skills practice, and bridging to traditional care. However, most robust empirical research has focused on adult populations, with pediatric applications only beginning to receive dedicated investigation. Key therapeutic mechanisms identified include reduced barriers to self-disclosure, cognitive change, emotional validation, and behavioral activation. Developmental considerations emerged as fundamental challenges, necessitating age-appropriate adaptations across cognitive, emotional, linguistic, and ethical dimensions rather than simple modifications of adult-oriented systems.

Conclusions: Conversational AI has potential to address significant unmet needs in pediatric mental health as a complement to, rather than replacement for, human-delivered care. Future research should prioritize developmental validation, longitudinal outcomes, implementation science, safety monitoring, and equity-focused design. Interdisciplinary collaboration involving children and families is essential to ensure these technologies effectively address the unique mental health needs of young people while mitigating potential risks.

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来源期刊

Children-Basel PEDIATRICS-

CiteScore

2.70

自引率

16.70%

发文量

1735

审稿时长

6 weeks

期刊介绍： Children is an international, open access journal dedicated to a streamlined, yet scientifically rigorous, dissemination of peer-reviewed science related to childhood health and disease in developed and developing countries. The publication focuses on sharing clinical, epidemiological and translational science relevant to children’s health. Moreover, the primary goals of the publication are to highlight under‑represented pediatric disciplines, to emphasize interdisciplinary research and to disseminate advances in knowledge in global child health. In addition to original research, the journal publishes expert editorials and commentaries, clinical case reports, and insightful communications reflecting the latest developments in pediatric medicine. By publishing meritorious articles as soon as the editorial review process is completed, rather than at predefined intervals, Children also permits rapid open access sharing of new information, allowing us to reach the broadest audience in the most expedient fashion.