Artificial Intelligence in Early Detection of Autism Spectrum Disorder for Preschool ages: A Systematic Literature Review

BackgroundEarly detection of autism spectrum disorder (ASD) improves outcomes, yet clinical assessment is time-intensive. Artificial intelligence (AI) may support screening in preschool children by analysing behavioural, neurophysiological, imaging, and biomarker data. AimTo synthesise studies that applied AI in ASD assessment and evaluate whether the underlying data and AI approaches can distinguish ASD characteristics in early childhood. MethodsA systematic search of 15 databases was conducted on 30 November 2024 using predefined terms. Inclusion criteria were empirical studies applying AI to ASD detection in children aged 0-7 years. Reporting followed PRISMA 2020. ResultsTwelve studies met criteria. Reported performance (AUC) ranged from 0.65 to 0.997. Modalities included behavioural (eye-tracking, home videos), motor (tablet/reaching), EEG, diffusion MRI, and blood/epigenetic biomarkers. The largest archival dataset (M-CHAT-R) achieved near-perfect AUC with neural networks. Common limitations were small samples, male-skewed cohorts, and limited external validation. ConclusionsAI can aid early ASD screening in infants and preschoolers, but larger and more diverse datasets, rigorous external validation, and multimodal integration are needed before clinical deployment.