ReviewPediatric research2025
The role of machine learning in autism spectrum disorder assessment and management.
Review in Pediatric research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Article
- A Critical Synthesis of Machine Learning in Autism Spectrum Disorder Genomic Research: From Transcriptomics to Microbiome.Medeniyet medical journal · 2026Article
- Review
- The future of neurodevelopmental disabilities.Pediatric research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism Spectrum Disorder (ASD) presents significant challenges in diagnosis and treatment, driven by its heterogeneous nature and complex aetiology. Recent advances in machine learning (ML) have facilitated exploration of novel approaches to ASD detection, stratification, and intervention opportunities. This narrative review explores the current ML and artificial intelligence (AI) research landscape across several key domains, including early ASD screening, phenotypic stratification, diagnostic biomarkers, neuroimaging, personalised therapies, and the role of automation and robotics in the treatment of this complex condition. Detailed analyses of these approaches emphasise the transformative but not yet realised potential of ML to improve outcomes for individuals with ASD. IMPACT: Highlights emerging trends, including multimodal AI integration, digital phenotyping, and use of AI to achieve biomarker-driven precision medicine. Provides first comprehensive synthesis of AI advancements in screening, diagnosis and treatment of ASD. Identifies current gaps in AI ASD research, such as dataset heterogeneity, validation issues, and clinical trust barriers.
Indexed as
Identifiers
41238901What 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.