Purpose or Case Report: Artificial intelligence (AI) has the potential to improve many aspects of pediatric radiology. AI solutions have emerged to perform a wide range of tasks, including facilitating the workload of radiologists, improving image quality, performing motion correction, and reducing contrast dose. While a myriad of courses for learning AI are available, most resources lack the necessary radiology perspective required for clinical application. Few interactive resources are available to teach a systematic approach for pediatric radiologists when analyzing AI literature. Since AI methodologies in pediatric radiology research fundamentally differ from classical statistical analyses, an educational poster dedicated to interpreting pediatric radiology AI literature would assist in bridging this gap.

The purpose of this education exhibit is to provide a resource for pediatric radiologists that teaches a systematic approach for the interpretation of AI research publications. Knowledge delivery will be made by eLearning slide-based presentation. The major topics regarding AI literature will focus on: problem identification; data curation; data annotation, artificial intelligence models; validation and performance. Next, we review prior literature on important criteria for AI publications, including Checklist for Artificial Intelligence in Medical Imaging (CLAIM), the AI version of the Standards for Reporting of Diagnostic Accuracy Studies (STARD-AI), Transparent Report of Multivariable Prediction Model of Individual Prognosis and Diagnosis for AI (TRIPOD-AI) and Prediction Model Risk of Bias Assessment Tool for AI (PROBAST-AI). Lastly, we present these criteria in the context of pediatric AI research applications.
AI research methodology in pediatric radiology differs from classical research methodologies. Therefore, this education exhibit is intended to teach the systematic interpretation of the primary AI literature to pediatric radiologists in order to translate scientific knowledge into meaningful clinical information.
Methods & Materials:
Results:
Conclusions: