Autism spectrum disorder (ASD) presents unique therapeutic and diagnostic challenges because of its heterogeneous and complex nature. In recent years, artificial intelligence (AI) models have been proposed as powerful tools for the early identification of ASD and more accurate design of supportive interventions. This review explores the application of AI techniques—including machine learning, deep learning, generative AI, and transformer models—in the context of ASD. These AI technologies have been used for predictive modeling, emotion and facial recognition, and the development of personalized care solutions. AI holds transformative potential in enhancing diagnosis and support for individuals with ASD. Moreover, this study addresses limitations such as data quality, generalizability, ethical concerns, and the need for human–AI collaboration. By synthesizing current advancements and challenges, this review aims to guide future research toward more responsible and effective AI integration in autism care.
Mahmoud et al. (Thu,) studied this question.
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