Abstract:: Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder characterized by complex pathophysiology involving social communication impairments, restricted interests, and repetitive behavior. Rising worldwide prevalence and symptom variability complicate accurate diagnosis and individualized treatment, posing significant challenges for clinicians. New evidence points towards multifactorial interactions—such as polygenic risk, environmental pollutants, and epigenetic deregulation—as major contributors to the etiology of ASD. This article intends to raise awareness of the potential of utilizing Artificial Intelligence (AI) to transform ASD diagnosis and treatment. By integrating machine learning techniques with multimodal neuroimaging, genetic, and behavioral data, we introduce AI-driven diagnostic systems for precise and early identification of ASD. We also discuss AI-supported customized treatments, including personalized behavioral and targeted pharmacological interventions. Our work highlights the promise that AI holds for reshaping the landscape of ASD care by providing timely and optimal interventions that improve outcomes for individuals with ASD and their families.
Madia et al. (Fri,) studied this question.
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