Artificial intelligence in environmental etiology of autism spectrum disorder: progress, opportunities, and challenges

Abstract

Traditional epidemiological analyses may be limited when addressing examining the environmental etiology of autism spectrum disorder (ASD), including assumptions of linearity, difficulties in handling high-dimensional exposure data, and challenges in detecting gene-environment or exposure-exposure interactions. The emergence of artificial intelligence (AI) - ranging from conventional machine learning to deep learning and large language models (LLMs) - has provided new tools for modeling the complex associations between environmental exposures and health outcomes. Machine learning models have shown strong predictive performance in individual-level air pollution exposure assessment and has enabled the detection of untargeted chemical substances from large-scale mass spectrometry data. In this review, we examine AI applications in four key areas of ASD environmental etiology research: environmental exposure assessment, ASD phenotyping, exposure-outcome association analysis, and risk prediction. Our findings suggest that although LLMs are emerging in environmental toxicology research, ASD environmental etiology studies remain predominantly based on conventional machine-learning methods, with deep learning and LLMs being less applied in this field. Further progress will require large-scale prospective cohort studies, adoption of advanced AI methods, unified data infrastructure, and interation across disciplines.

Keywords

Autism spectrum disorder / environmental exposure / AI / machine learning / risk prediction / causal inference

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