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- W4381487670 abstract "Several radiology artificial intelligence (AI) courses are offered by a variety of institutions and educators. The major radiology societies have developed AI curricula focused on basic AI principles and practices. However, a specific AI curriculum focused on pediatric radiology is needed to offer targeted education material on AI model development and performance evaluation. There are inherent differences between pediatric and adult practice patterns, which may hinder the application of adult AI models in pediatric cohorts. Such differences include the different imaging modality utilization, imaging acquisition parameters, lower radiation doses, the rapid growth of children and changes in their body composition, and the presence of unique pathologies and diseases, which differ in prevalence from adults. Thus, in order to enhance radiologists’ knowledge in the applications of AI models in pediatric patients, curricula should be structured keeping in mind the unique pediatric setting and its challenges, along with methods to overcome these challenges, and pediatric-specific data governance and ethical considerations. In this paper, we aim to highlight the salient aspects of pediatric radiology which are necessary for AI education in the pediatric setting, including the challenges for research investigation and clinical implementation." @default.
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- W4381487670 date "2023-08-01" @default.
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- W4381487670 title "Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development" @default.
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- W4381487670 doi "https://doi.org/10.1016/j.jacr.2023.04.013" @default.
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