SynthesisFrontiers in neuroscience2026
Artificial intelligence for autism spectrum disorder: advances in diagnosis, behavior analysis and educational support.
Synthesis in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Introduction: Artificial intelligence has become an increasingly relevant field of research in the study of Autism Spectrum Disorder (ASD), offering novel technological approaches for the analysis, detection, and support of individuals on the autism spectrum. The aim of this study was to systematically review recent scientific literature examining the application of artificial intelligence in ASD. Methods: The review was conducted following the PRISMA 2020 guidelines. Searches were performed in PubMed, Scopus, Dialnet, and Google Scholar, including studies published between 2019 and 2025. After applying predefined inclusion and exclusion criteria, 18 empirical studies were included in the final analysis. Methodological quality and risk of bias were assessed using Joanna Briggs Institute critical appraisal tools adapted to the methodological design of each study. Results: Current research focuses primarily on four areas: early detection and diagnostic support, automated analysis of behavioral and social patterns, AI-based educational technologies, and communication support systems. Although the reviewed studies demonstrate promising advances in machine learning, computer vision, and natural language processing, important methodological limitations remain, particularly regarding external validation, dataset representativeness, and heterogeneity of performance indicators. Discussion: Overall, artificial intelligence shows considerable potential for supporting diagnosis, education, and communication in ASD; however, greater methodological robustness, transparency, and ethical safeguards remain necessary before broader implementation in real clinical and educational settings.
Indexed as
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.