Jun 2026· Pediatric Investigation· 0 citations· 88 references
Medicine
TL;DR
AI shows promise for pediatric NDD care, but equitable clinical adoption will require inclusive research, external validation, and equity‐focused implementation, with particular attention to equity considerations.
Abstract
ABSTRACT Artificial intelligence (AI) is increasingly being used in healthcare and has the potential to improve the diagnosis, treatment, monitoring, and management of neurodevelopmental disorders (NDDs) in children. Early identification and personalized care are often constrained by subjective assessment and inequitable access. AI tools may enhance diagnostic accuracy and care delivery; however, the maturity, clinical readiness, and equity implications of this evidence base remain unclear. This scoping review mapped peer‐reviewed studies published in English since 2015 that described empirical or conceptual AI applications for diagnosis, monitoring, decision support, or treatment in pediatric NDD care, with particular attention to equity considerations. From 1027 records, 13 studies met the inclusion criteria. Most focused on attention deficit/hyperactivity disorder (ADHD, n = 7), autism spectrum disorder (ASD, n = 4), with predominating diagnostic tools. Reported accuracies ranged from 76% to 100% for ADHD and from 88% to 95% for ASD. Most studies were preliminary or in early implementations, with only one externally validated. Equity considerations were limited, with overrepresentation of White males and little attention paid to socioeconomic or cultural factors. AI shows promise for pediatric NDD care, but equitable clinical adoption will require inclusive research, external validation, and equity‐focused implementation.
It is argued that AI’s most important contribution to autism care is unlikely to lie in marginal improvements in classification accuracy alone, and its potential value lies in expanding access, supporting task-sharing, shortening diagnostic pathways, enabling population-oriented screening, and reaching under-recognised groups.
Xugao Han, Lingyan Weng, Houxi Xu· Frontiers in Public Health· 0 citations
Autism spectrum disorder (ASD) is a developmental disorder that must be identified early but traditional diagnosis can be examiner-dependent, time-consuming and not accessible. This study included a dual bibliometric and systematic review on the evidence supporting the application of artificial intelligence (AI) tools in the detection of ASD according to PRISMA 2020 and SWiM guideline. A total of 41 empirical studies were identified after a search in the Scopus database. The studies were conducted between 2011 and 2026, in 11 data modalities, 7 algorithm families and 28 countries. The accuracy of the reports was between 68.18% and 100% with a median of 91%. Facial image research was the biggest modality, but all nine studies used the same public Kaggle dataset. Unstated validation designs occurred in 46% of studies, and 93% were at high risk of bias in the areas of patient selection and reference standards, and unclear flow and timing. Very high accuracy scores were massed in small, one cohort datasets, with population level studies obtaining significantly lower accuracy scores. But in general, there was only a small difference in median accuracy between the externally validated studies and the single-cohort studies (1.5 percentage points). The results suggest that the use of AI to detect ASD is not yet ready for clinical use. Transparent reporting of validation, independent external cohorts, diverse datasets, and prospective testing of positive predictive value in realistic population prevalence should be the focus of future research.
Shivani Pant, Anita Gehlot, Neha Singh et al.· International Journal for Gl...· 0 citations
These technologies show promise in reducing human error and enhancing mental health care delivery; however, persistent challenges include data privacy, ethical considerations, and the need for diverse, large-scale datasets.
Juster Donal Sinaga· Journal of Society Counselin...· 0 citations
A comprehensive review of recent advancements in ASD research, with particular emphasis on neuroimaging, artificial intelligence (AI), and machine learning (ML)-based diagnostic approaches, highlights the growing potential of AI-driven tools for supporting early ASD diagnosis and emphasizes the need for standardized protocols, external validation, explainable AI, and clinically translatable frameworks.
Kuljeet Singh, Khushi Mogha, S. Moctar· Neurological Sciences· 0 citations
Artificial intelligence shows promise as a supportive tool for early screening, but current evidence supports its use as a complement to, rather than replacement for, clinical assessment.
Andrea Catalina Mahecha Ballesteros, Juanita Valeria García Bello, Eleaine Scarlet González Zuñiga et al.· Current Psychiatry Reports· 0 citations
The increased prevalence of ASD has generated a pressing demand for flexible therapeutic and educational tools. AI has been suggested as a potential bridge to this gap, but the translation from a model to a clinical application necessitates rigorous assessment. The purpose of the present review is to compile and examine the existing literature to demonstrate AI interventions that have advanced from a proposed model to being used with human participants.
We systematically searched five databases (Embase, PubMed, ScienceDirect, IEEE Xplore, Web of Science; Jan 2016–Dec 2025) for AI-based ASD interventions. Two reviewers independently assessed eligibility. Inclusion criteria were as followed: (1) participants with confirmed ASD diagnoses; (2) an intervention sample size of N ≥ 6; (3) AI as a central therapeutic, educational, or rehabilitative component; and (4) multi-session protocols with specified timeframes. Study types ranged from system development and feasibility trials to RCTs.
14 studies met inclusion criteria. AI (e.g. robotics, VR, and wearables) functioned as a social mediator, improving social-emotional outcomes (e.g., ADOS, SRS scores) by reducing cognitive load. Significant mechanisms included real-time task adaptation and precise behavioral monitoring via e.g. eye-tracking. However, significant heterogeneity was observed in intervention dosage (median 4–12 hours). Most studies were limited by small, male-dominated samples (N < 20) and a total absence of adult participants.
Based on the findings, AI seems highly promising for patient-specific ASD therapy via proactive, data-driven scaffolding. In order to move toward implementation of AI in ASD care, more RCTs and crucial augmentation of representation gaps concerning adult and female ASD phenotype studies are required.
Unknown authors· Frontiers in Psychiatry· 0 citations
