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Impact <p><UnorderedList Mark="Bullet"> <ItemContent> <p>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).</p> </ItemContent> <ItemContent> <p>These technological advancements collectively demonstrate ML’s potential to complement—rather than replace—expert clinical assessment in the screening and diagnosis of ASD.</p> </ItemContent> <ItemContent> <p>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.</p> </ItemContent> </UnorderedList></p>

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

  • 期刊论文

  • 发表日期：

    2025年12月16日

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  • Khaled Saad,
  • Soha A. Hussain,
  • Ahmad Roshdy Ahmad,
  • Mohamed Shawky Elfarargy,
  • Amira Elhoufey,
  • Abdulrahman A. Al-Atram,
  • Abdelrahman N. Abdelal,
  • Kawashty R. Mohamed,
  • Mostafa M. Embaby

摘要

Impact

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.