Artificial intelligence for detecting autism spectrum disorder: a systematic review and meta-analysis

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition, and early identification is essential for timely intervention. Conventional diagnosis relies on clinical interviews, behavioral observations, caregiver reports, and standardized tools, which are time-consuming and resource-dependent. Artificial intelligence (AI) offers opportunities for screening and adjunctive diagnosis, but studies vary in data sources, model types, and validation methods, requiring systematic evaluation.

We searched PubMed, Embase, the Cochrane Library, Web of Science, and IEEE Xplore for studies of AI-based ASD detection. Eligible studies were required to provide ASD-versus-non-ASD diagnostic-accuracy data that could be extracted or reliably reconstructed at the independent participant or case level. Risk of bias and applicability were assessed using a modified QUADAS-AI tool. Pooled sensitivity and specificity were estimated with a bivariate Reitsma random-effects model. HSROC AUCs were calculated with bias-corrected and accelerated (BCa) 95% confidence intervals based on 5,000 study-level bootstrap replicates and were omitted when SROC fits were unstable. Sensitivity analyses addressed dataset overlap, model selection, and reference-standard quality.

Of 17,387 records, 26 studies were included. Pooled sensitivity was 0.908 (95% CI: 0.861-0.940), pooled specificity was 0.868 (95% CI: 0.830-0.898), and the HSROC AUC was 0.936 (BCa 95% CI: 0.860-0.973). A dataset-independent analysis of 21 studies yielded lower estimates (sensitivity 0.870, specificity 0.841, AUC 0.900). Leave-one-out results were generally stable. Funnel-plot asymmetry was not significant in the primary analysis and varied in direction and statistical significance across sensitivity analyses.

AI-based models showed relatively high pooled diagnostic performance for ASD detection, but substantial heterogeneity, methodological and applicability concerns, limited external validation, and potential non-independence of underlying datasets limit the certainty and generalizability of the evidence. Funnel-plot analyses did not provide consistent evidence of conventional small-study effects or selective reporting. AI should therefore be considered an adjunct rather than a standalone diagnostic tool. Prospective multicenter validation in representative populations, participant-level evaluation, clearly defined reference diagnoses, prespecified models and operating thresholds, and independent evaluation datasets are needed before broader clinical implementation.