A systematic review for artificial intelligence-driven assistive technologies to support children with neurodevelopmental disorders

Abstract

This systematic review examines AI-powered assistive technologies for children with neurodevelopmental disorders, with a focus on dyslexia (DYS), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Our analysis of 84 studies from 2018 to 2024 provides the first thorough cross-disorder comparison of AI implementation patterns. According to our data, each condition has different success rates and technological preferences. AI applications are expanding quickly, especially in research on ASD (56 % of studies), followed by ADHD (36 %), and DYS (8 %). In almost half of the reviewed studies, computer-assisted technologies, which have demonstrated encouraging results in terms of treatment support and diagnostic accuracy, became the main mode of intervention. Despite high accuracy in controlled settings, the implementation of these technologies in clinical practice faces significant challenges. While human oversight remains essential in clinical applications, future advancements should prioritize privacy protection and the ability to assess tools longitudinally. Notably, multimodal approaches that integrate various data types have improved diagnostic accuracy; recent research has shown that they can detect ASD with up to 99.8 % accuracy and ADHD with up to 97.4 % accuracy. A promising trend is the combination of mobile applications and wearable technology, especially for real-time monitoring and intervention. This review highlights the potential and current limitations of AI-driven tools in supporting children with neurodevelopmental disorders. Future development should focus not on replacing clinical expertise, but on augmenting it. Research efforts should aim at creating tools that enhance professional judgment while preserving the essential human components of assessment and intervention.