ArticleNature communications2026
Automated screening of autism via a pretrained vision-language model in naturalistic caregiver-child interactions.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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9 authors.
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Abstract
Early autism screening is essential for timely intervention but remains constrained by specialist dependence, costly protocols, and limited accessibility. We present Autism-CLIP, a vision-language model that automates screening from 3-min naturalistic caregiver-child interaction videos recorded in toddlers aged 15-24 months. By integrating contrastive learning, large language model-generated supervision, domain-specific architecture, and hybrid training, Autism-CLIP aligns visual behavioral cues with autism-related assessment outcomes, enabling annotation-free inference. In multicenter evaluation, Autism-CLIP achieved an AUC of 0.903 in the internal cohort (n = 103) and 0.873 in the external cohort (n = 102), outperforming conventional machine learning methods. Attention mapping highlighted clinically relevant markers, including gaze aversion and limited social reciprocity. Requiring only smartphone-captured videos without manual coding or controlled settings, Autism-CLIP offers a scalable, low-cost approach to broaden access to early autism screening and support prioritization of clinical resources.
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