The current state of artificial intelligence research in pediatric radiology and recommendations for the future: a scoping review.
Authors
Affiliations (24)
Affiliations (24)
- University of Oxford, Oxford, United Kingdom. [email protected].
- Department of Medical Imaging, University of Toronto, Toronto, Canada. [email protected].
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Evanston, United States.
- Faculty of Medicine, University of Ottawa, Ottawa, Canada.
- School of Medicine, McMaster University, Hamilton, Canada.
- Faculty of Medicine, University of British Columbia, Vancouver, Canada.
- Department of Family and Community Medicine, University of Toronto, Toronto, Canada.
- Department of Immunology, University of Toronto, Toronto, Canada.
- Ottawa Hospital Research Institute (OHRI), Ottawa Hospital, Ottawa, Canada.
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Canada.
- Department of Pediatrics, University of Ottawa, Ottawa, Canada.
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, United States.
- Department of Radiology, University of Cincinnati, Cincinnati, United States.
- Department of Radiology, Texas Children's Hospital, Houston, United States.
- Department of Radiology, University of Washington, Seattle, United States.
- Department of Radiology, Harvard University, Cambridge, United States.
- Department of Radiology, Nationwide Children's Hospital, Columbus, United States.
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, United States.
- The Hospital for Sick Children, Department of Diagnostic Imaging, 555 University Avenue, Toronto, ON, M5G1X8, Canada.
- Department of Radiology, University of Pennsylvania, Philadelphia, United States.
- Department of Radiology, University of Missouri-Kansas City, Kansas City, United States.
- Department of Medical Imaging, University of Toronto, Toronto, Canada. [email protected].
- The Hospital for Sick Children, Department of Diagnostic Imaging, 555 University Avenue, Toronto, ON, M5G1X8, Canada. [email protected].
Abstract
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. 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. 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. 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). 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.