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Introduction: The growing demand for earlier diagnosis of neurodevelopmental disorders has boosted critical assessment of artificial intelligence (AI) as a complementary tool for clinical decision-making. Methods: This umbrella review aimed to synthesize the available evidence from systematic reviews and meta-analyses on the use of AI to diagnose during childhood any neurodevelopmental disorder autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), intellectual disability, communication disorders, developmental coordination disorder, and specific learning disorders. A systematic search was conducted on the Web of Science, PsycINFO, and PubMed, covering studies published from January 2015 to August 2025 and available in any language. Results: = 11). Overall, these AI models achieved diagnostic accuracy levels ranging from 66% (based on head/facial/eye movements) to 99% (based on neuroimaging, voice, motion, and sensors). However, the methodological quality of most studies was rated as critically low according to the AMSTAR-2 criteria (80%), while only 5% of studies achieved high quality levels (focused on ASD and ADHD). Conclusion: AI shows promising potential for supporting biomarker identification and diagnosis of neurodevelopmental disorders. However, future clinical implementation still requires methodologically rigorous research addressing current limitations: insufficient external validation, lack of standardization in data collection and model development, as well as reporting inconsistencies. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251110825, identifier CRD420251110825.
Alberca-González et al. (Wed,) studied this question.