The evolving role of machine learning in autism spectrum disorder: current evidence and future directions.

Abstract

Machine learning (ML) has become a key factor in advancing artificial intelligence (AI)-driven strategies across various areas in recent years, including screening, diagnosis, subtyping, and therapeutic intervention in autism spectrum disorder (ASD). These technological advancements collectively demonstrate ML's potential to complement-rather than replace-expert clinical assessment in the screening and diagnosis of ASD. Future research should focus on standardizing data collection procedures, improving the interpretability of models, and conducting multi-center validation studies to confirm their effectiveness and applicability in real-world settings.