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

Researchers are exploring how computer technology (machine learning) can help with autism screening, diagnosis, and treatment planning. Early results show promise, but this technology is meant to support doctors and specialists, not replace them. More research is needed to make sure these tools work well in real healthcare settings and can be trusted by families and professionals.

This review examines machine learning applications in autism spectrum disorder, covering screening, diagnosis, subtyping, and therapeutic interventions. The authors emphasize that ML shows promise as a complementary tool to enhance clinical assessment rather than replace expert evaluation. Key areas of development include automated screening systems, diagnostic support tools, and personalized intervention approaches. The review identifies critical research priorities including standardizing data collection methods, improving model interpretability for clinical use, and conducting rigorous multi-center validation studies to establish real-world effectiveness and generalizability across diverse populations and clinical settings.

Machine learning tools may enhance clinical decision-making in autism care but require standardized implementation and validation. Clinicians should view these as supportive technologies rather than replacements for expert assessment. Multi-center studies needed before widespread clinical adoption.

The abstract does not specify sample sizes, methodology details, or quantitative outcomes. No information provided about the scope of studies reviewed or quality assessment criteria used.

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.