Redefining Autism Care: Innovative Solutions for Autism Diagnosis and Intervention

Autism is a neurological disease that has serious effects on a child's social, cognitive, and emotional intelligence. It is optimal for children with autism to be diagnosed at a young age and receive treatment/intervention to enhance these traits. However, the intervention methods for children with autism remain largely ineffective in the United States. Traditional methods are often slow, resource-intensive, and yield inconsistent developmental outcomes, resulting in a 68% graduation rate and an average hourly income of $8.10, which is significantly lower compared to other disabled groups. Technology, over the years, has allowed researchers to understand the flaws of the developmental system for autistic children and use innovative measures to fix it. This paper conducts a cross-study examination of existing research to highlight improvement through innovative measures regarding intervention methods and early diagnosis, as well as systemic barriers that hinder a quality developmental process. Through extensive innovation using machine learning, researchers have built predictive models that have broken the age barrier of early diagnosis, establishing an intelligent screening system that analyzes brain imaging and genetic data to diagnose a child with autism at a few months old instead of the late age of two years old. Early interventions include Naturalistic Developmental Behavioral Interventions (NDBI) and Applied Behavior Analysis (ABA), which are effective in promoting the social and cognitive development of autistic children. Researchers have also highlighted socio-economic aspects that impact an autistic child's development, such as the classroom environment and systemic barriers like limited clinician access, household income, etc. Future directions include the conceptualization of a low-cost virtual persona and AI agencies designed for personalized intervention and continuous evaluation, acknowledging the latest AI technologies. This approach aims to bridge existing gaps in autism education by combining technological innovation with evidence-based practices to improve long-term developmental outcomes and reduce the cost.