Applying Artificial Intelligence in Diagnosis and Treatment of Autism Spectrum Disorder in Children

Abstract

Autism Spectrum Disorder (ASD) is a disorder of increasing prevalence that affects individuals socially, emotionally, and academically. The increased prevalence and restricted access to diagnosis and treatment suggest more efficient and widely accessible services are necessary. Many individuals with ASD do not receive proper attention due to various reasons, including costs, long wait lists, and long processes. Recent developments in artificial intelligence and machine learning are believed to be able to aid this accessibility issue. Research has shown progress in using MRI and EEG datasets to develop machine learning models in diagnosing ASD and potentially finding biomarkers using supervised and unsupervised ML techniques. AI algorithms analyzing body language and physical behaviors could potentially be used to assess ASD characteristics despite the heterogeneity of the disorder. The adaptivity of artificial intelligence is believed to have the potential to create supportive software for students with ASD to support learning, emotional regulation, and development of social communication skills and increased adaptability. More evidence is required to prove the effectiveness of these applications, but many studies show a lot of promise for children with ASD.