Objective To analyze the application status, research hotspots, and evolving trends of AI in childhood neurodevelopmental disorders. Methods English articles published from January 1, 2010 to May 31, 2026 were retrieved from the Web of Science Core Collection.Excel, VOSviewer, and CiteSpace were used to analyze publication trends, collaboration networks, keyword co-occurrence, and keyword bursts.Multi-label content coding was performed based on titles, abstracts, and keywords. Results A total of 1 776 articles were included.The annual number of publications increased overall, with marked growth after 2018 and a peak in 2025 (n=297).The United States (n=480) and China (n=451) were the leading contributors.Keyword co-occurrence analysis identified three major clusters: autism spectrum disorder(ASD) early screening, social behavior recognition, and intelligent intervention; attention-deficit/hyperactivity(ADHD)-related brain function assessment and deep learning; and learning/language disorder recognition and feature extraction.Keyword burst analysis identified deep learning, heterogeneity, natural language processing, severity assessment, working memory, and graph neural networks as recent hotspots.Content coding showed that studies mainly focused onASD (1 228, 69.14%) andADHD(430, 24.21%).Disease identification and assisted diagnosis (1 374, 77.36%) was the most common application.Data sources included neuroimaging, electroencephalogram, visual behavior, speech and language, eye tracking, movement trajectories, behavioral scales, and robot/virtual reality interaction.Traditional machine learning (1 148, 64.64%) and deep learning/neural network-based methods (670, 37.73%) were the predominant methods. Conclusions Research on AI in childhood neurodevelopmental disorders has continued to increase.Current studies mainly focus on diseaseidentification and assisted diagnosis, and have gradually extended to early screening, multimodal assessment, intelligent intervention, heterogeneity identification, and recognition of learning/language disorders. Further studies should strengthen multicenter research and validation, so as to provide evidence for early identification, assessment, and intervention of childhoodneurodevelopmental disorders.
Key words
artificial intelligence /
neurodevelopmental disorders /
bibliometrics /
machine learning /
deep learning /
children
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