Applying artificial intelligence in neurodevelopmental disorders management and research

Abstract

Artificial intelligence (AI) is increasingly being used in the diagnosis, treatment, and monitoring of neurodevelopmental disorders, enabling earlier detection, personalised interventions, and continuous support. Traditional machine-learning models such as logistic regression, random forests, and support vector machines remain valuable for their interpretability and their ability to integrate multimodal clinical data. Deep-learning (DL) approaches, including convolutional neural networks and transformer-based architectures, improve the analysis of neuroimaging and behavioural datasets and strengthen diagnostic and prognostic performance. Important challenges remain, including limited transparency in DL systems, ongoing concerns about data privacy and algorithmic bias, and a lack of large and diverse paediatric datasets that restricts generalisability. Interpretability tools such as SHAP and LIME offer partial solutions but still lack standardised evaluation. At the same time, AI-driven robotic platforms are enhancing therapeutic engagement and supporting skill acquisition in children with neurodevelopmental conditions. This review highlights that AI tools have strong potential to act as clinical adjuncts rather than replacements, providing earlier detection, personalised management, and scalable care models. Realising this potential will require rigorous validation, ethical safeguards, and thoughtful integration into human-led care pathways.

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