ARTIFICIAL INTELLIGENCE APPLICATIONS IN PEDIATRIC MENTAL HEALTH: SYSTEMATIC REVIEW OF SCREENING, DIAGNOSIS, AND TREATMENT MONITORING TECHNOLOGIES (Global Conference on Medical and Health Sciences, 2026)
Background: Pediatric mental health disorders affect approximately 13% of children globally, yet access to specialist evaluation remains severely limited. Artificial intelligence (AI) technologies offer potential to augment screening, diagnostic assessment, and treatment monitoring in pediatric psychiatry. Objective: To systematically review evidence on AI applications in pediatric mental health across the spectrum from population screening to treatment optimization. Methods: Systematic review of studies published 2020–2025 was conducted across five databases. AI tools evaluated included computerized adaptive testing, machine learning classifiers, natural language processing, and wearable sensor analysis. Results: Fifty-eight studies met inclusion criteria. AI screening tools demonstrated sensitivity of 82–94% for depression and anxiety, 78–91% for ADHD, and 85–93% for autism spectrum disorder. Treatment response prediction achieved AUC-ROC of 0.80–0.92 across mood disorders. Conclusion: AI demonstrates meaningful efficacy across pediatric mental health applications while raising important ethical questions regarding data privacy, algorithmic bias, and the appropriate role of technology in child psychiatric care.
Keywords: pediatric mental health, artificial intelligence, machine learning, autism spectrum disorder, ADHD, depression screening, digital health, child psychiatry