Personalized rehabilitation in autism spectrum disorder: artificial intelligence technologies for diagnosis, prediction, and adaptive interventions

Autism spectrum disorders (ASDs) represent a heterogeneous group of neurodevelopmental conditions. Diagnosis and treatment of autism carry high social significance, necessitating the development of effective rehabilitation strategies. Artificial intelligence (AI) technologies offer fundamentally new opportunities to personalize rehabilitation interventions in ASD. This comprehensive review examines key directions for AI integration into clinical practice: enhancing diagnostic objectivity through digital phenotyping, predicting individual developmental trajectories and treatment responses, and designing adaptive therapeutic systems. Particular attention is paid to AI-assisted technologies for developing social communication skills, including virtual reality and social robotics. Meta-analytic data confirm the clinical utility of AI technologies, showing moderate-to-large effects for social communication, emotion recognition, and adaptive skills. Naturalistic developmental behavioral interventions, combined with augmentative and alternative communication technologies have been shown to demonstrate sustained improvements in spontaneous communication in minimally verbal patients. The review discusses prospects for personalizing interventions based on physiological monitoring data and prognostic modeling along with the role of telehealth technologies and intelligent family support systems. Critical challenges of AI system implementation are analyzed, ranging from privacy risks to the translational gap between research and clinical practice. Overall, maximizing the potential of AI in ASD rehabilitation requires a balanced approach combining accurate and explainable algorithms, inclusive user design, and interdisciplinary collaboration with active involvement of autistic individuals and their families at all stages of frontier technology development and implementation.

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