Use of Artificial Intelligence for the identification of sings and characteristics of Autism Spectrum Disorder
DOI:
https://doi.org/10.33448/rsd-v15i2.50637Keywords:
Autism Spectrum Disorder, Artificial Intelligence, Early Diagnosis, Mental Health.Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent impairments in social communication and by the presence of restricted and repetitive patterns of behavior, interests, or activities, with significant impact on the individual’s adaptive functioning. In this context, Artificial Intelligence (AI) has emerged as a promising technological resource, especially due to its ability to process large volumes of data and identify complex patterns through machine learning techniques. The present study aimed to analyze, through an integrative literature review, advances in the use of Artificial Intelligence for the identification of signs and characteristics of Autism Spectrum Disorder. The search was conducted in the PubMed database. After applying the inclusion and exclusion criteria, as well as a careful analysis of the abstracts, 13 studies were selected to compose the final sample. The results indicate that AI has been mainly applied in the analysis of behavioral patterns, eye movements, facial expressions, biometric data, and biomarkers, showing potential to assist in screening and supporting ASD diagnostic processes. However, the literature highlights relevant methodological limitations, such as dependence on the quality and heterogeneity of the data used to train the algorithms, as well as ethical challenges related to privacy, information security, and potential algorithmic biases. It is concluded that Artificial Intelligence constitutes a complementary tool to specialized clinical assessment, requiring methodological rigor, empirical validation, and integration into multiprofessional practices for its responsible application.
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