Mansoor Masab, Hamide Ali, Tran Tyler
Edward Via College of Osteopathic Medicine-Louisiana Campus, Monroe, LA 71203, USA.
Children (Basel). 2025 Mar 14;12(3):359. doi: 10.3390/children12030359.
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.
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.
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.
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.
背景/目的：儿童和青少年的心理健康障碍是一项重大的全球健康挑战，约50%的病症在14岁之前出现。尽管在服务方面投入巨大，但诸如提供者短缺、污名化和可及性问题等持续存在的障碍仍限制了有效护理的提供。本叙述性综述探讨了对话式人工智能（AI）在儿科心理健康领域的新兴应用，梳理当前的证据基础，确定治疗机制，并探索实施所需的独特发展考量。
我们检索了多个电子数据库（PubMed/MEDLINE、PsycINFO、ACM数字图书馆、IEEE Xplore和Scopus），以查找2010年1月至2025年2月期间发表的涉及与儿科心理健康相关的对话式AI应用的文献。我们采用叙述性综合方法和主题分析，以跨技术方法、治疗应用、发展考量、实施背景和伦理框架来组织研究结果。
该综述确定了对话式AI在儿科心理健康方面的有前景的应用，特别是对于焦虑和抑郁等常见病症、心理教育、技能练习以及与传统护理的衔接。然而，大多数有力的实证研究集中在成人人群，儿科应用才刚刚开始受到专门研究。确定的关键治疗机制包括降低自我表露的障碍、认知改变、情感确认和行为激活。发展考量成为基本挑战，需要在认知、情感、语言和伦理层面进行适合年龄的调整，而不是简单修改面向成人的系统。
对话式AI有潜力满足儿科心理健康中重大的未满足需求，作为对人工提供护理的补充而非替代。未来研究应优先进行发展验证、纵向结果研究、实施科学、安全监测和以公平为重点的设计。涉及儿童和家庭的跨学科合作对于确保这些技术有效满足年轻人独特的心理健康需求同时降低潜在风险至关重要。
