ArticleNature medicine2023
Early detection of autism using digital behavioral phenotyping.
Article in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 4 of them syntheses that pooled it.
What it found
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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
51 citing papers in PubMed, 4 syntheses or guidelines pooled it, 97 citations in OpenAlex.
- Artificial Intelligence Methods in Early Detection of Autism Spectrum Disorder: A DSM-5 Criterion-Based Systematic Review.Autism research : official journal of the International Society for Autism Research · 2026Pooled it
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Prevalence of children's mental health disorders and implications in pharmacotherapy: a systematic review and meta-analysis.Postgraduate medicine · 2025Pooled it
- Early diagnostic value of home video-based machine learning in autism spectrum disorder: a meta-analysis.European journal of pediatrics · 2024Pooled it
- Organoid intelligence: a promising paradigm for autism spectrum disorder research.Molecular psychiatry · 2026Review
- Early Detection Methods for Autism Spectrum Disorder: From Clinical Screening to Multimodal AI.Diagnostics (Basel, Switzerland) · 2026Review
- Smartphone movement data can reliably predict smoking lapses and cravings to enable timely smoking cessation support.Scientific reports · 2026Article
- Smartphone gaze-tracking for accessible psychiatric assessment.Npj mental health research · 2026Article
- Data-Driven Neurocognitive Clustering Predicts Virtual Reality Task Performance in Children: A Pilot Study.Brain sciences · 2026Article
- Autism outcomes and neurobehavioural markers in young children born to mothers with HIV in Kenya: a protocol for the Alama project.BMJ open · 2026Article
- Rapid test for detecting red-green color vision deficiencies using a neural network-assisted color-naming task.Scientific reports · 2026Article
- Naturalistic facial dynamics enable quantitative clinical assessment of atypical expression phenotypes in children with autism spectrum disorder.NPJ digital medicine · 2026Article
- ICYM... International Conference on Learning Representations · 2026Article
- A naturalistic, non-invasive method for capturing biometric data during autism evaluations.Frontiers in psychiatry · 2026Article
- Artificial intelligence, autism care, and health equity: a public health narrative review.Frontiers in public health · 2026Review
- Artificial-intelligence-based developmental screening for pre-school children (ages 3-6): a scoping review of deployed tools, clinical validity, equity, and implementation in guidance-center settings.Frontiers in psychology · 2026Review
- AI-supported early autism identification as public health infrastructure: economic implications for the United States using a scenario-based economic model.Frontiers in public health · 2026Article
- Digital screening of children with ASD: diagnostic accuracy of emotion recognition and visual preference tasks.BMC psychiatry · 2025Article
- AI-Enabled Technologies and Biomarker Analysis for the Early Identification of Autism and Related Neurodevelopmental Disorders.Children (Basel, Switzerland) · 2025Review
- Using generative AI for the objective assessment of language in healthcare.Scientific reports · 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
11 authors at 2 institutions in 2 countries.
Funding
Abstract
Early detection of autism, a neurodevelopmental condition associated with challenges in social communication, ensures timely access to intervention. Autism screening questionnaires have been shown to have lower accuracy when used in real-world settings, such as primary care, as compared to research studies, particularly for children of color and girls. Here we report findings from a multiclinic, prospective study assessing the accuracy of an autism screening digital application (app) administered during a pediatric well-child visit to 475 (17-36 months old) children (269 boys and 206 girls), of which 49 were diagnosed with autism and 98 were diagnosed with developmental delay without autism. The app displayed stimuli that elicited behavioral signs of autism, quantified using computer vision and machine learning. An algorithm combining multiple digital phenotypes showed high diagnostic accuracy with the area under the receiver operating characteristic curve = 0.90, sensitivity = 87.8%, specificity = 80.8%, negative predictive value = 97.8% and positive predictive value = 40.6%. The algorithm had similar sensitivity performance across subgroups as defined by sex, race and ethnicity. These results demonstrate the potential for digital phenotyping to provide an objective, scalable approach to autism screening in real-world settings. Moreover, combining results from digital phenotyping and caregiver questionnaires may increase autism screening accuracy and help reduce disparities in access to diagnosis and intervention.
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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.