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Artificial Intelligence-Based Mobile Phone Apps for Child Mental Health: Comprehensive Review and Content Analysis.

Fan Yang¹, Jianan Wei², Xuejun Zhao³

¹School of Social Work, University of Illinois Urbana Champaign, Urbana, IL, United States.

JMIR Mhealth and Uhealth

|June 6, 2025

Summary

Artificial intelligence (AI) mental health apps for children show promise but need better design and affordability. Most apps lack child-friendly features and rigorous clinical testing, limiting their effectiveness and accessibility.

Keywords:

AI-driven mobile applications artificial intelligence children mental health mobile health mobile phone

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Area of Science:

Digital Health
Child and Adolescent Psychiatry
Artificial Intelligence in Healthcare

Background:

Artificial intelligence (AI) powered mobile applications are increasingly utilized for addressing mental health concerns in children.
These digital tools offer novel avenues for intervention and support in pediatric mental healthcare.

Purpose of the Study:

To conduct a comprehensive review of AI-driven mobile applications designed for child mental health.
The review focused on app availability, quality, readability, characteristics, and functional categories.

Main Methods:

Systematic analysis of AI-based mobile apps for child mental health.
App quality was assessed using the Mobile Application Rating Scale (MARS).
Readability was evaluated using an automatic index calculator; content analysis examined app characteristics and functions.

Main Results:

Out of 369 identified apps, 27 were included. Average MARS score was 3.45/5, indicating quality improvement needs.
Readability scores were suboptimal (average grade level 6.62 in-app, 9.93 in app stores), with monotonous interfaces.
Apps were categorized as chatbots (15), journal logging (9), or psychotherapeutic (3). 74% used validated technologies, but only 2 underwent clinical trials. 74% required payment (average $20.16/month).

Conclusions:

AI mental health apps for children have significant potential but face limitations in design, accessibility, and validation.
Future development must prioritize child-centric design, affordability, and rigorous clinical testing.
These improvements are crucial for equitable and effective AI-driven solutions in child mental health.