ReviewJournal of the Korean Academy of Child and Adolescent Psychiatry2025
Trends in Multimodal Artificial Intelligence for Autism Research in Children and Adolescents: A Scientometric Study.
Review in Journal of the Korean Academy of Child and Adolescent Psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Early Detection Methods for Autism Spectrum Disorder: From Clinical Screening to Multimodal AI.Diagnostics (Basel, Switzerland) · 2026Review
- Contrastive and Transfer Learning for Aligned Multimodal Neuroimaging Classification of Autism Spectrum Disorder.Journal of imaging · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using scientometric and content-based approaches, this study explored the thematic structure and knowledge evolution in the application of artificial intelligence (AI) to autism spectrum disorder (ASD) in children and adolescents. A comprehensive search was conducted in the Scopus database up to June 30, 2025, using combined keyword sets related to ASD, AI, and child development, and 1401 English-language articles were retained. A bibliometric analysis was performed using VOSviewer to identify keyword co-occurrence patterns and citation linkages. The resulting thematic clusters were further analyzed through qualitative content examination. The analysis revealed a rapid growth in publications since 2015, with a surge after 2020, reflecting a growing interest in AI applications for ASD. Four important thematic clusters emerged: 1) AI-driven diagnostic models, 2) neuroimaging-based analysis, 3) early risk detection and behavioral assessment, and 4) emerging research on interventions and comorbidities, which remains comparatively limited. The United States, China, and the United Kingdom led in contributions, supported by active international collaborations. Highly cited and high-link-strength studies focused mainly on the use of deep learning in neuroimaging and early detection. Overall, the field demonstrates a strong diagnostic focus, increasing methodological sophistication, and interdisciplinary integration across computer science, neuroscience, and developmental psychology, leaving such areas as intervention and longitudinal tracking underexplored. These findings offer a comprehensive overview of the current landscape of the field and indicate critical opportunities for future development.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.