Scholarlink

Abstract <p>Autism Spectrum Disorder (ASD) presents significant challenges in diagnosis and treatment, driven by its heterogeneous nature and complex aetiology. Recent advances in machine learning (ML) have facilitated exploration of novel approaches to ASD detection, stratification, and intervention opportunities. This narrative review explores the current ML and artificial intelligence (AI) research landscape across several key domains, including early ASD screening, phenotypic stratification, diagnostic biomarkers, neuroimaging, personalised therapies, and the role of automation and robotics in the treatment of this complex condition. Detailed analyses of these approaches emphasise the transformative but not yet realised potential of ML to improve outcomes for individuals with ASD.</p> Impact <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Highlights emerging trends, including multimodal AI integration, digital phenotyping, and use of AI to achieve biomarker-driven precision medicine.</p> </ItemContent> <ItemContent> <p>Provides first comprehensive synthesis of AI advancements in screening, diagnosis and treatment of ASD.</p> </ItemContent> <ItemContent> <p>Identifies current gaps in AI ASD research, such as dataset heterogeneity, validation issues, and clinical trust barriers.</p> </ItemContent> </UnorderedList></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The role of machine learning in autism spectrum disorder assessment and management

  • 期刊论文

  • 发表日期：

    2025年11月14日

查看全文
  • Aoife Reilly,
  • Nicola Walsh,
  • Daniel O’Reilly,
  • Miriam Smyth,
  • Kathleen Gorman,
  • Sarah Ostadabbas,
  • Claire Power

摘要

Abstract

Autism Spectrum Disorder (ASD) presents significant challenges in diagnosis and treatment, driven by its heterogeneous nature and complex aetiology. Recent advances in machine learning (ML) have facilitated exploration of novel approaches to ASD detection, stratification, and intervention opportunities. This narrative review explores the current ML and artificial intelligence (AI) research landscape across several key domains, including early ASD screening, phenotypic stratification, diagnostic biomarkers, neuroimaging, personalised therapies, and the role of automation and robotics in the treatment of this complex condition. Detailed analyses of these approaches emphasise the transformative but not yet realised potential of ML to improve outcomes for individuals with ASD.

Impact

Highlights emerging trends, including multimodal AI integration, digital phenotyping, and use of AI to achieve biomarker-driven precision medicine.

Provides first comprehensive synthesis of AI advancements in screening, diagnosis and treatment of ASD.

Identifies current gaps in AI ASD research, such as dataset heterogeneity, validation issues, and clinical trust barriers.