Scholarlink

Background <p>Most artificial intelligence (AI) research in radiology has focused on adults. Understanding macro-level trends in pediatric radiology AI can help guide, streamline, and bolster future research.</p> Objective <p>To detail the current landscape of published AI research in pediatric radiology, filling a key research gap, as most radiology AI research has focused on adults.</p> Materials and methods <p>We conducted a scoping review, with a comprehensive literature search of Medline, Embase, Web of Science, and Cochrane Library from 2005 to 2024. Literature included for review were (1) original articles, (2) investigations that focused on pediatric populations (&lt;18&#xa0;years of age), and (3) articles with direct applications to clinical radiology and AI. We extracted each article’s study information, clinical application of focus, imaging modality, and the use of AI. We used descriptive frequencies to analyze summary statistics, and Chi-square testing to determine differences between categories.</p> Results <p>In total, we found 4,376 articles and included 789 articles in the review. The top three countries most active in scholarship related to AI in pediatric radiology were China (220, 27.9%), the USA (200, 25.4%), and Canada (51, 6.5%) (<i>P</i>&lt;0.001). The most common imaging modalities were radiography (298, 37.8%), MRI (260, 33.0%), and ultrasonography (114, 14.4%) (<i>P</i>&lt;0.001). The most common subspecialties represented were musculoskeletal (260, 33.0%), neurological (227, 28.8%), and chest imaging (130, 16.5%) (<i>P</i>&lt;0.001). The top two image analysis tasks discussed were image interpretation/diagnosis (719, 91.1%), and artifact and motion reduction/enhancing image quality (44, 5.6%) (<i>P</i>&lt;0.001).</p> Conclusion <p>Most pediatric radiology AI research originated from China and the USA, and focused on image interpretation/diagnosis. Thematic imbalances, particularly underrepresentation in research on communication, education, policy, and stakeholder perspectives, offer a guide for pediatric radiology AI development. There is a need for improved global collaboration and improved patient representativeness in datasets for pediatric radiology AI research to reduce bias with AI algorithms. The results from this scoping review offer a practical roadmap to inform future research and funding priorities in pediatric radiology AI.</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 current state of artificial intelligence research in pediatric radiology and recommendations for the future: a scoping review

  • 期刊论文

  • 发表日期：

    2026年1月24日

查看全文
  • Rakhshan Kamran,
  • Elysa Widjaja,
  • Alex Sy,
  • Jessica Bosso,
  • Lomesh Choudhary,
  • Alexandra Lawrynuik,
  • Yu Xuan Jin,
  • Cynthia Chan,
  • Nasana Vaidya,
  • Sarah Larrigan,
  • Liam Jackman,
  • Yujin Suk,
  • Laura Larrigan,
  • Ann Lee,
  • Geetika Khanna,
  • Andrew Trout,
  • Marla Sammer,
  • Randolph Otto,
  • Michael Gee,
  • Cara Morin,
  • Mai-Lan Ho,
  • Meghna Gaddam,
  • Hansel Otero,
  • Sara Reis Teixeira,
  • M. Alejandra Bedoya,
  • Andy Tsai,
  • Savvas Andronikou,
  • Sherwin Chan,
  • Andrea S. Doria

摘要

Background

Most artificial intelligence (AI) research in radiology has focused on adults. Understanding macro-level trends in pediatric radiology AI can help guide, streamline, and bolster future research.

Objective

To detail the current landscape of published AI research in pediatric radiology, filling a key research gap, as most radiology AI research has focused on adults.

Materials and methods

We conducted a scoping review, with a comprehensive literature search of Medline, Embase, Web of Science, and Cochrane Library from 2005 to 2024. Literature included for review were (1) original articles, (2) investigations that focused on pediatric populations (<18 years of age), and (3) articles with direct applications to clinical radiology and AI. We extracted each article’s study information, clinical application of focus, imaging modality, and the use of AI. We used descriptive frequencies to analyze summary statistics, and Chi-square testing to determine differences between categories.

Results

In total, we found 4,376 articles and included 789 articles in the review. The top three countries most active in scholarship related to AI in pediatric radiology were China (220, 27.9%), the USA (200, 25.4%), and Canada (51, 6.5%) (P<0.001). The most common imaging modalities were radiography (298, 37.8%), MRI (260, 33.0%), and ultrasonography (114, 14.4%) (P<0.001). The most common subspecialties represented were musculoskeletal (260, 33.0%), neurological (227, 28.8%), and chest imaging (130, 16.5%) (P<0.001). The top two image analysis tasks discussed were image interpretation/diagnosis (719, 91.1%), and artifact and motion reduction/enhancing image quality (44, 5.6%) (P<0.001).

Conclusion

Most pediatric radiology AI research originated from China and the USA, and focused on image interpretation/diagnosis. Thematic imbalances, particularly underrepresentation in research on communication, education, policy, and stakeholder perspectives, offer a guide for pediatric radiology AI development. There is a need for improved global collaboration and improved patient representativeness in datasets for pediatric radiology AI research to reduce bias with AI algorithms. The results from this scoping review offer a practical roadmap to inform future research and funding priorities in pediatric radiology AI.

Graphical Abstract