Purpose or Case Report: Artificial intelligence (AI) applications for radiology have undergone exponential growth in recent years, owing to the development of large datasets for use in machine learning algorithms and technological advancements in the field of imaging informatics. However, the advancement of AI algorithms in pediatric radiology has lagged behind adult applications. Currently, only seven commercially-available AI algorithms have received FDA approval for use in the pediatric population [1]. One of the major factors limiting the use of AI in pediatric radiology is the lack of the requisite large pediatric imaging datasets.
In AI research and implementation, pediatric radiologists serve as stewards of imaging data. As such, pediatric radiologists should be trained in AI data management, including best practices for the selection, curation, de-identification, and storage of radiology data. Since a necessary first step in the development of AI algorithms requires the curation of large datasets, pediatric radiologists should have a basic understanding of how to archive imaging data for AI research and validation. However, few resources are currently available to provide targeted education for pediatric radiologists with respect to AI data curation..
The aim of this educational exhibit is to provide an educational resource specifically for pediatric radiologists which teaches best practices for data management in AI research, including the selection of patient cohorts, data anonymization techniques, image annotation and segmentation methods, and data storage tools. This exhibit integrates our professional experience, with a thorough literature review of prior AI research, into an educational resource to teach data science methodologies for the management of AI research and clinical implementation to the pediatric radiology community.

References
1. AI Central. https://aicentral.acrdsi.org/. Accessed 18 Oct 2022
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