Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup

Marla B.K. Sammer, Yasmin S. Akbari, Richard A. Barth, Steven L. Blumer, Jonathan R. Dillman, Shannon G. Farmakis, Don P. Frush, Ami Gokli, Safwan S. Halabi, Ramesh Iyer, Aparna Joshi, Jeannie K. Kwon, Hansel J. Otero, Andrew C. Sher, Susan T. Sotardi, Benjamin H. Taragin, Alexander J. Towbin, Christoph Wald

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In this white paper, the ACR Pediatric AI Workgroup of the Commission on Informatics educates the radiology community about the health equity issue of the lack of pediatric artificial intelligence (AI), improves the understanding of relevant pediatric AI issues, and offers solutions to address the inadequacies in pediatric AI development. In short, the design, training, validation, and safe implementation of AI in children require careful and specific approaches that can be distinct from those used for adults. On the eve of widespread use of AI in imaging practice, the group invites the radiology community to align and join Image IntelliGently (www.imageintelligently.org) to ensure that the use of AI is safe, reliable, and effective for children.

Original languageEnglish (US)
Pages (from-to)730-737
Number of pages8
JournalJournal of the American College of Radiology
Volume20
Issue number8
DOIs
StatePublished - Aug 2023

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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