AI-Driven Early Detection of Autism Spectrum Disorder in American Children

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopment disorder that impacts 1 in 36 children in the United States. Therefore, early identification plays a pivotal role. Nonetheless, such populations mentioned may take late and wrong screenings, thus further amplifying disparities. This study aims to create a screening model based on AI with multisource data: EHR, developmental diagnostic checklists, and wearing device behavioural data to improve the early diagnosis of ASD. Based on a cohort of more than 20,000 children, this work showcases how AI can enhance the diagnostics of conditions and lessen the time to diagnosis and bias in children’s care (Alzakari et al.,2024). By integrating both sources, the overall accuracy was 91%, thus making the model better than single-source models, and there are encouraging prospects for the large-scale deployment of the proposed system in developing/least developed countries. Potential solutions and recommendations regarding AI applications in pediatric healthcare, as well as several ethical considerations and upcoming issues concerning scalability, are also examined.