A multimodal large language model for evidence-based autism spectrum disorder screening

2026-09-14

AI Summary

为解决自闭症谱系障碍早期筛查瓶颈，提出ASDchat模型，采用视频、音频和对话作为输入，基于双分支架构进行筛查并生成行为证据。

Abstract

The clinical management of autism spectrum disorder (ASD) faces a bottleneck in early screening, mainly because trained specialists are scarce and conventional assessment tools are subjective. Here, we introduce ASDchat, a multimodal large language model designed for evidence-based ASD screening, which takes video, audio, and dialogue as input. ASDchat adopts a dual-branch architecture, where the decision branch generates screening probabilities and the evidence branch generates traceable, timestamped behavioral evidence aligned with standardized clinical criteria (ADOS-2). The model was trained and evaluated on a dataset of 1,035 participants from 27 sites in China, which covered typically developing (TD) children, children with ASD, and children with other disorders. For ASD versus TD, ASDchat reached an area under the receiver operating characteristic curve (AUC) of 0.953 $\pm$ 0.021. On 9 held-out sites that were not used for training, the mean AUC was 0.932. Furthermore, unsupervised clustering of the behavioral dimensions split the ASD cases into six subtypes with different phenotypic profiles, and ASDchat suggests an intervention for each subtype. ASDchat provides a feasible path for large-scale, evidence-based early ASD screening in clinical practice.

Problem

• autism spectrum disorder
• early screening
• trained specialists
• conventional assessment tools

Innovation

• multimodal large language model
• evidence-based ASD screening
• dual-branch architecture
• behavioral evidence generation
• unsupervised clustering