Artificial Intelligence in Child and Adolescent Psychiatry: A Narrative Review of Recent Clinical Applications and Ethical Considerations

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported — no pooled quantitative synthesis performed. Chatbots and robot companions described as providing 'modest improvements' for depression symptoms in paediatric populations.

• Primary Outcome: Most AI applications in child and adolescent psychiatry remain early in development and are not ready for routine clinical use. AI-enabled video and eye-tracking for autism diagnosis is identified as one of the few exceptions showing sufficient promise.

• Nnt Or Sensitivity: Not reported for any specific AI tool. Diagnostic accuracy metrics for individual studies (e.g., sensitivity/specificity for eye-tracking in autism diagnosis) are not pooled or summarised with precision in the abstract.

• Confidence Interval: Not reported

Clinical Application

Implementation feasibility varies substantially by AI modality. AI scribes face significant practical barriers in child psychiatry including multiparty consent, sensitive disclosures, and developmental communication differences. Neuroimaging-dependent diagnostic tools are cost-prohibitive for most clinical settings. Eye-tracking for autism diagnosis and chatbot-based mental health support represent more feasible near-term applications, though both require further validation and governance frameworks before routine use. Australia faces a well-documented child and adolescent mental health workforce crisis, with RACGP and RANZCP guidelines acknowledging significant unmet need, particularly in rural and remote settings. The TGA regulates software as a medical device (SaMD) under the Therapeutic Goods (Medical Devices) Regulations 2002, and any AI diagnostic or therapeutic tool would require appropriate classification and registration before clinical deployment. The PBS does not currently fund AI-assisted psychiatric tools. The Australian Digital Health Agency's National Digital Health Strategy 2023–2028 provides a policy framework relevant to AI integration. Aboriginal and Torres Strait Islander youth, who experience disproportionate mental health burden, are particularly vulnerable to algorithmic bias from AI systems trained on non-representative datasets — a critical consideration absent from this review. The headspace and CAMHS service models in Australia may offer structured environments for supervised AI pilot programs consistent with the review's recommendations. Children and adolescents presenting to psychiatric and mental health services, particularly those with autism spectrum disorder, depression, and anxiety. Also applicable to child and adolescent psychiatrists, paediatricians, GPs, and allied health professionals considering or encountering AI-assisted tools in clinical workflows.

Abstract

PURPOSE OF REVIEW: This narrative review examines recent literature of artificial intelligence (AI) in child and adolescent psychiatry. With increasing mental health disorders in young people along with persistent workforce shortages, AI has emerged as a potential tool to improve efficiency and support clinical decision making. However, using AI raises important ethical concerns which are summarized in our review. RECENT FINDINGS: Most AI applications in child and adolescent psychiatry remain early in development and are not ready for routine clinical use. AI scribes are likely impractical in many child psychiatry settings because of multiparty visits, consent concerns, and sensitive clinical discussions. Many multimodal diagnostic tools using machine learning still require further testing and can be impractical when relying on costly diagnostics like neuroimaging. Similarly, AI-assisted therapeutics requiring physical hardware like robotics and virtual or augmented reality devices can also be prohibitively expensive. One of the few exceptions includes AI-enabled video and eye-tracking approaches for autism diagnosis. Chatbots and robot companions may provide modest improvements for depression, but evidence remains limited in the pediatric population with risk of serious harm. Concerns of AI include misinformation, algorithmic biases, privacy risks, crisis mismanagement, and excessive emotional attachments to chatbots. AI may eventually support child and adolescent psychiatry, but current evidence supports cautious, supervised use rather than broad clinical adoption. Clinicians should help families understand AI's limits, encourage digital literacy, and ensure that AI remains an adjunct to human care rather than a substitute.

References

1. 1.Cheng, S., Kim, E., Bhalodi, R., Um, T., Wong, D., & Yuen, E. Y. (2026). Artificial intelligence in child and adolescent psychiatry: A narrative review of recent clinical applications and ethical considerations. Current Psychiatry Reports. https://doi.org/10.5888/pcd21.240245