ReviewJournal of personalized medicine2021
A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder.
Review in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Artificial intelligence support for diagnosis of neurodevelopmental disorders during childhood: an umbrella review.Frontiers in psychiatry · 2026Pooled it
- Prediction models for highly scalable technology-assisted differential diagnostics of autism spectrum disorder.BMJ mental health · 2026Article
- The assessing medical professionals' knowledge of autism spectrum disorders.Journal of public health research · 2026Article
- Explainable AI for early developmental disability detection: a neuro-fuzzy approach.Frontiers in public health · 2026Article
- Explainable AI uncovers novel EEG microstate candidate neurophysiological markers for autism spectrum disorder.Frontiers in computational neuroscience · 2026Article
- A Brief Observation to Screen Autism in Toddlers and Predict Developmental Trajectory.Journal of pediatrics. Clinical practice · 2025Article
- Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10.Scientific reports · 2025Article
- Fast and effective assessment for individuals with special needs form optimization and prediction models.BMC psychology · 2025Article
- Latest clinical frontiers related to autism diagnostic strategies.Cell reports. Medicine · 2025Review
- Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review.Alcohol, clinical & experimental research · 2024Review
- Breaking Barriers-The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review.Journal of personalized medicine · 2023Review
- An Umbrella Review of the Fusion of fMRI and AI in Autism.Diagnostics (Basel, Switzerland) · 2023Review
- A data driven machine learning approach to differentiate between autism spectrum disorder and attention-deficit/hyperactivity disorder based on the best-practice diagnostic instruments for autism.Scientific reports · 2022Article
- Autism Spectrum Disorders and inclusion attitudes in the Italian school environments: teachers' knowledge, attitudes, perceptions and their necessity to consult a healthcare multidisciplinary team.Acta bio-medica : Atenei Parmensis · 2022Article
- Special Issue "Precision Medicine in Neurodevelopmental Disorders: Personalized Characterization of Autism from Molecules to Behavior".Journal of personalized medicine · 2022Article
- The role of AI-driven art therapy in supporting autism, mental health, and emotional well-being: An umbrella review.Digital healthReview
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 at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies have utilized behavioral data in developing and evaluating the performance of machine learning (ML) models toward quick and intelligent ASD assessment systems. However, despite the good evaluation metrics achieved by the ML models, there is not enough evidence on the readiness of the models for clinical use. Specifically, none of the existing studies reported the real-life application of the ML-based models. This might be related to numerous challenges associated with the data-centric techniques utilized and their misalignment with the conceptual basis upon which professionals diagnose ASD. The present work systematically reviewed recent articles on the application of ML in the behavioral assessment of ASD, and highlighted common challenges in the studies, and proposed vital considerations for real-life implementation of ML-based ASD screening and diagnostic systems. This review will serve as a guide for researchers, neuropsychiatrists, psychologists, and relevant stakeholders on the advances in ASD screening and diagnosis using ML.
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.