Automated screening of autism via a pretrained vision-language model in naturalistic caregiver-child interactions

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.