Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development.

Autor:	Alkhulaifat D; Department of Pediatric Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania., Rafful P; Department of Pediatric Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania., Khalkhali V; Department of Pediatric Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania., Welsh M; Department of Pediatric Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania., Sotardi ST; Director, CHOP Radiology Informatics and Artificial Intelligence, Department of Pediatric Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: sotardis@chop.edu.
Jazyk:	angličtina
Zdroj:	Journal of the American College of Radiology : JACR [J Am Coll Radiol] 2023 Aug; Vol. 20 (8), pp. 724-729. Date of Electronic Publication: 2023 Jun 21.
DOI:	10.1016/j.jacr.2023.04.013
Abstrakt:	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, to enhance radiologists' knowledge of 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 report, the authors highlight the salient aspects of pediatric radiology that are necessary for AI education in the pediatric setting, including the challenges for research investigation and clinical implementation. (Copyright © 2023 American College of Radiology. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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