Scholarlink

<p>Artificial intelligence (AI) is increasingly shaping radiology, though its integration into paediatric radiology has progressed more slowly due to challenges specific to the paediatric population. This is especially true in the field of paediatric abdominal imaging. Key barriers include regulatory and ethical issues, the scarcity of large paediatric datasets necessary for algorithm training, reduced vendor interest linked to limited economic incentives, and the inherent differences in children throughout the developmental stages including organ size, signal/sonographic characteristics, and pathologies. Despite these obstacles, AI has the potential to enhance clinical care by augmenting radiologists’ workflow across both interpretive and non-interpretive tasks. Currently, most published research focuses on AI’s role in musculoskeletal imaging. Although AI is expanding its reach in other imaging domains, paediatric imaging lags behind, as does its potential in abdominal imaging. The use of AI in paediatric abdominal imaging has received limited attention in the existing literature. Emerging research applications cover multiple tasks: detection, classification, functional analysis, severity prediction, automated segmentation, image quality optimization, and acceleration of image acquisition. This review aims to provide practicing radiologists with a concise, simple, and clinically oriented overview of the potential applications and limitations of these new AI tools in paediatric abdominal imaging, categorized by organ. For the time being, most applications described in the literature remain confined to the research setting. To advance these approaches towards clinical utility, validation on larger and more heterogeneous datasets is required. Moving forward, it will be essential to integrate human expertise with AI systems to strengthen diagnostic capacity in paediatric abdominal radiology and to promote paediatric-specific regulatory standards, clear governance structures, and human-centred oversight.</p> Graphical abstract <p></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 artificial intelligence in paediatric abdominal imaging

  • 期刊论文

  • 发表日期：

    2026年3月11日

查看全文
  • Ione Limantoro,
  • Samual Stafrace,
  • Ilze Apine,
  • Carmelo Sofia,
  • Seema Toso,
  • Damjana Kljucevsek,
  • Giulia Perucca

摘要

Artificial intelligence (AI) is increasingly shaping radiology, though its integration into paediatric radiology has progressed more slowly due to challenges specific to the paediatric population. This is especially true in the field of paediatric abdominal imaging. Key barriers include regulatory and ethical issues, the scarcity of large paediatric datasets necessary for algorithm training, reduced vendor interest linked to limited economic incentives, and the inherent differences in children throughout the developmental stages including organ size, signal/sonographic characteristics, and pathologies. Despite these obstacles, AI has the potential to enhance clinical care by augmenting radiologists’ workflow across both interpretive and non-interpretive tasks. Currently, most published research focuses on AI’s role in musculoskeletal imaging. Although AI is expanding its reach in other imaging domains, paediatric imaging lags behind, as does its potential in abdominal imaging. The use of AI in paediatric abdominal imaging has received limited attention in the existing literature. Emerging research applications cover multiple tasks: detection, classification, functional analysis, severity prediction, automated segmentation, image quality optimization, and acceleration of image acquisition. This review aims to provide practicing radiologists with a concise, simple, and clinically oriented overview of the potential applications and limitations of these new AI tools in paediatric abdominal imaging, categorized by organ. For the time being, most applications described in the literature remain confined to the research setting. To advance these approaches towards clinical utility, validation on larger and more heterogeneous datasets is required. Moving forward, it will be essential to integrate human expertise with AI systems to strengthen diagnostic capacity in paediatric abdominal radiology and to promote paediatric-specific regulatory standards, clear governance structures, and human-centred oversight.

Graphical abstract