Clinical Applicability and Effectiveness of AI-driven Interventions for Children with Neurodevelopmental and Motor Disorders: A Systematic Review and Meta-analysis of Randomized Clinical Trials

Abstract

Artificial intelligence (AI)-driven technologies are increasingly deployed in pediatric rehabilitation for children with neurodevelopmental and motor disorders, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and cerebral palsy (CP). Despite the rapid increase in AI-enabled interventions across clinical and home settings, the evidence base for clinical applicability, effectiveness, and implementation requirements remains insufficiently characterized. This systematic review and meta-analysis aimed to: (i) describe the types of AI technologies and intervention models used across diagnostic groups and settings; (ii) summarize reported effects on child outcomes; and (iii) critically appraise clinical applicability, including feasibility, acceptability, safety, equity considerations, and key implementation requirements. A systematic review and meta-analysis of randomized controlled trials (RCTs) evaluating AI-driven or AI-assisted interventions in children and adolescents (≤18 years of age) with ADHD, ASD, or CP was conducted. Five databases (MEDLINE, Embase, PsycINFO, CINAHL, Web of Science) were searched for studies published between 2020 and 2025. Two independent reviewers screened the studies, extracted data, and assessed the risk of bias using the Cochrane Risk of Bias 2 (RoB2) tool. Effect sizes were calculated as Hedges’ g using random-effects models, with subgroup analyses conducted for three intervention–condition pairs. Fifteen RCTs were included, enrolling approximately 1039 participants (ages 3-18 years) across nine countries. Six AI technology categories were identified: adaptive digital therapeutics ( n = 3), robot-assisted therapy ( n = 6), virtual reality systems ( n = 2), robotic exoskeletons ( n = 2), neurofeedback ( n = 1), and mobile cognitive training ( n = 1). The International Classification of Functioning, Disability and Health for Children and Youth (ICF-CY) mapping revealed that 62.5% of studies targeted body functions outcomes and 37.5% targeted activities outcomes. A meta-analysis of eight studies yielded a statistically significant overall pooled effect [Hedges’ g = 0.26 (95% confidence interval: 0.04, 0.49), P = 0.02, I 2 = 40%]. Clinical applicability varied substantially: digital therapeutics demonstrated highest feasibility and scalability for home-based delivery, while robotic exoskeletons showed limited accessibility due to cost and infrastructure requirements. Equity concerns were identified, with 14 of 15 studies conducted in high-income countries. AI-driven interventions produce statistically significant, small-to-moderate benefits across ADHD, ASD, and CP, with effect sizes varying by technology type and diagnostic group. These technologies should be considered adjunctive tools within multimodal rehabilitation programs rather than standalone replacements for conventional therapies. Future research priorities include larger trials with long-term follow-up, standardized functional outcome measures, equity-focused study designs in diverse populations and settings, and implementation research examining cost-effectiveness and integration into existing service systems.