Leveraging AI for the diagnosis and treatment of autism spectrum disorder: Current trends and future prospects

Abstract

The integration of artificial intelligence (AI) into the diagnosis and treatment of autism spectrum disorder (ASD) represents a promising frontier in healthcare. This review explores the current landscape and future prospects of AI technologies in ASD diagnostics and interventions. AI enables early detection and personalized assessment of ASD through the analysis of diverse data sources such as behavioural patterns, neuroimaging, genetics, and electronic health records. Machine learning algorithms exhibit high accuracy in distinguishing ASD from neurotypical development and other developmental disorders, facilitating timely interventions. Furthermore, AI-driven therapeutic interventions, including augmentative communication systems, virtual reality-based training, and robot-assisted therapies, show potential in improving social interactions and communication skills in individuals with ASD. Despite challenges such as data privacy and interpretability, the future of AI in ASD holds promise for refining diagnostic accuracy, deploying telehealth platforms, and tailoring treatment plans. By harnessing AI, clinicians can enhance ASD care delivery, empower patients, and advance our understanding of this complex condition.