Artificial Intelligence-Powered Approaches for Autism Care: Techniques, Applications, and Future Directions

Document Type : Refereed research papers.

Author

Lecturer of Computer, at MET Academy - Misr Higher Institute for Commerce and Computers, Mansoura

10.21608/aiis.2025.459876

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that affects communication, social interaction, and behavior. Rising prevalence rates worldwide place increasing challenges on families and clinicians, who often struggle to achieve early and reliable diagnosis. Conventional assessments, though valuable, are resource-intensive, subjective, and limited in scalability. Recent advances in Artificial Intelligence (AI) provide new opportunities to address these challenges. Techniques such as machine learning, deep learning, reinforcement learning, natural language processing (NLP), and generative approaches are being applied to improve early detection, personalize interventions, and develop assistive tools. These innovations hold promise for supporting clinicians, educators, and caregivers in both clinical and everyday contexts. However, persistent barriers —including limited datasets, algorithmic bias, and concerns about transparency, privacy, and ethics— continue to constrain broader adoption. Future progress will rely on multimodal, explainable, and culturally sensitive AI systems, validated through longitudinal and real-world studies. This review highlights both the potential and the responsibility of applying AI to autism care, emphasizing collaboration among researchers, practitioners, and families to ensure equitable and sustainable innovation.

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