The Role of Artificial Intelligence in the Early Diagnosis and Screening of Autism Spectrum Disorder in Young Children

Abstract

This article explores the potential applications of artificial intelligence (AI) in the early diagnosis and screening of Autism Spectrum Disorder (ASD). It outlines contemporary methodologies such as video analysis, speech recognition, and functional brain imaging, which have emerged as promising tools in identifying ASD at earlier stages.

ASD hinders the development of children's social, communicative, and behavioral skills. Early diagnosis and timely intervention play a vital role in shaping developmental trajectories and improving quality of life. Traditional diagnostic methods are largely dependent on the expertise of psychiatrists, and they often require substantial time and resources. Consequently, most children with ASD receive a clinical diagnosis before the age of six. Early intervention programs facilitate the development of essential competencies and contribute to a reduction in the severity of clinical symptoms.

In this context, artificial intelligence - particularly machine learning and deep learning techniques -plays a pivotal role in enhancing diagnostic precision, reducing the time and resource burden, and increasing overall efficiency. AI-supported psychosocial interventions promote the development of communication and social skills, thereby positively influencing the well-being and life satisfaction of both individuals with ASD and their caregivers. The aim of this study is to evaluate the effectiveness of AI-based models in processing behavioral, linguistic, visual, and biometric data to determine their accuracy and reliability in ASD-related applications.