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

doi:10.1016/j.jacr.2023.06.003

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

J Am Coll Radiol. 2023 Aug;20(8):730-737. doi: 10.1016/j.jacr.2023.06.003. Epub 2023 Jul 25.

Authors

Marla B K Sammer¹, Yasmin S Akbari², Richard A Barth³, Steven L Blumer⁴, Jonathan R Dillman⁵, Shannon G Farmakis⁶, Don P Frush⁷, Ami Gokli⁸, Safwan S Halabi⁹, Ramesh Iyer¹⁰, Aparna Joshi¹¹, Jeannie K Kwon¹², Hansel J Otero¹³, Andrew C Sher¹⁴, Susan T Sotardi¹³, Benjamin H Taragin¹⁵, Alexander J Towbin¹⁶, Christoph Wald¹⁷

Affiliations

¹ Singleton Department of Radiology, Texas Children's Hospital, Houston, Texas; Department of Radiology, Baylor College of Medicine, Houston, Texas; and Chair, Pediatric AI Workgroup, Commission on Informatics, American College of Radiology. Electronic address: mbsammer@texaschildrens.org.
² Radia, Lynnwood, Washington.
³ Department of Radiology, Lucile Packard Children's Hospital, Stanford University, Palo Alto, California.
⁴ Chair, Pediatrics Panel of the American College of Radiology Data Science Institute; Associate Medical Director of Radiology Informatics, University of Pittsburgh Medical Center Health System, Pittsburgh, Pennsylvania; and Department of Radiology, University of Pittsburgh Medical Center, Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania.
⁵ Associate Chief, Research, William S. Ball Chair of Radiology Research, and Medical Director, Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio; and University of Cincinnati College of Medicine, Cincinnati, Ohio.
⁶ Department of Radiology, Mercy Children's Hospital, St. Louis, Missouri; and West County Radiological Group, St. Louis, Missouri.
⁷ Duke University, Durham, North Carolina.
⁸ Division Chief of Pediatric Radiology, Staten Island University Hospital, Staten Island, New York; and Associate Program Director, Department of Radiology Residency Program, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, New York.
⁹ University of Chicago, Lurie Children's Hospital, Chicago, Illinois.
¹⁰ University of Washington, Seattle Children's Hospital, Seattle, Washington.
¹¹ Section of Pediatric Radiology, University of Michigan, Ann Arbor, Michigan.
¹² University of Texas Southwestern Medical Center, Dallas, Texas; Chief Radiology Officer, Children's Health System of Texas, Dallas, Texas; and Chair, Informatics Committee, Society for Pediatric Radiology.
¹³ University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
¹⁴ Singleton Department of Radiology, Texas Children's Hospital, Houston, Texas; and Department of Radiology, Baylor College of Medicine, Houston, Texas.
¹⁵ Department of Pediatric Radiology, Samson Assuta Ashdod University Hospital, Ashdod, Israel; Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; and Medical School for International Health, Ben Gurion University, Be'er Sheva, Israel.
¹⁶ Department of Radiology, Cincinnati Children's Hospital, Cincinnati, Ohio; and Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio.
¹⁷ Chair, Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts; Tufts University Medical School, Boston, Massachusetts; and Chair, Commission on Informatics.

PMID: 37498259
DOI: 10.1016/j.jacr.2023.06.003

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.

MeSH terms

Adult
Artificial Intelligence*
Child
Diagnostic Imaging / methods
Humans
Radiography
Radiology* / methods
Societies, Medical