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Conversational AI in Pediatric Mental Health: A Narrative Review
Masab Mansoor1, Ali Hamide1, Tyler Tran1
1Edward Via College of Osteopathic Medicine-Louisiana Campus, Monroe, LA 71203, USA.
Insights
Conversational artificial intelligence (AI) shows promise for improving pediatric mental health services by complementing human care. Further research is needed to ensure age-appropriate and equitable implementation for children and adolescents.
Area of Science:
- Pediatric mental health
- Conversational artificial intelligence (AI)
- Digital mental health interventions
Background:
- Pediatric mental health disorders are a global challenge, with many conditions emerging before age 14.
- Existing services face barriers like provider shortages, stigma, and accessibility issues.
- Conversational AI presents a novel approach to address these gaps in care delivery.
Purpose of the Study:
- To review the current evidence on conversational AI applications in pediatric mental health.
- To identify therapeutic mechanisms and developmental considerations for AI implementation.
- To explore the potential of AI to complement existing mental health services for youth.
Main Methods:
- Conducted a narrative review of literature from January 2010 to February 2025.
- Searched major electronic databases including PubMed/MEDLINE, PsycINFO, ACM Digital Library, IEEE Xplore, and Scopus.
- Utilized thematic analysis to synthesize findings on technology, therapy, development, implementation, and ethics.
Main Results:
- Conversational AI shows potential for anxiety, depression, psychoeducation, and skills practice in youth.
- Pediatric AI research is nascent, with most robust studies focusing on adult populations.
- Key mechanisms include reduced disclosure barriers, emotional validation, and behavioral activation; developmental adaptations are crucial.
Conclusions:
- Conversational AI can supplement, not replace, human mental health care for children and adolescents.
- Future research must focus on developmental validation, longitudinal outcomes, safety, equity, and implementation science.
- Interdisciplinary collaboration with families is vital for effective and safe AI integration in pediatric mental health.
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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