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

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

Medical School for International Health

Research output: Contribution to journal › Article › peer-review

57 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 language	English
Pages (from-to)	730-737
Number of pages	8
Journal	Journal of the American College of Radiology
Volume	20
Issue number	8
DOIs	https://doi.org/10.1016/j.jacr.2023.06.003
State	Published - 1 Aug 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 10 Reduced Inequalities

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging

Access to Document

10.1016/j.jacr.2023.06.003

Cite this

Sammer, M. B. K., Akbari, Y. S., Barth, R. A., Blumer, S. L., Dillman, J. R., Farmakis, S. G., Frush, D. P., Gokli, A., Halabi, S. S., Iyer, R., Joshi, A., Kwon, J. K., Otero, H. J., Sher, A. C., Sotardi, S. T., Taragin, B. H., Towbin, A. J., & Wald, C. (2023). Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup. Journal of the American College of Radiology, 20(8), 730-737. https://doi.org/10.1016/j.jacr.2023.06.003

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

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.",

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

note = "Publisher Copyright: {\textcopyright} 2023 American College of Radiology",

year = "2023",

month = aug,

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doi = "10.1016/j.jacr.2023.06.003",

language = "English",

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Sammer, MBK, Akbari, YS, Barth, RA, Blumer, SL, Dillman, JR, Farmakis, SG, Frush, DP, Gokli, A, Halabi, SS, Iyer, R, Joshi, A, Kwon, JK, Otero, HJ, Sher, AC, Sotardi, ST, Taragin, BH, Towbin, AJ & Wald, C 2023, 'Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup', Journal of the American College of Radiology, vol. 20, no. 8, pp. 730-737. https://doi.org/10.1016/j.jacr.2023.06.003

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T2 - Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup

AU - Sammer, Marla B.K.

AU - Akbari, Yasmin S.

AU - Barth, Richard A.

AU - Blumer, Steven L.

AU - Dillman, Jonathan R.

AU - Farmakis, Shannon G.

AU - Frush, Don P.

AU - Gokli, Ami

AU - Halabi, Safwan S.

AU - Iyer, Ramesh

AU - Joshi, Aparna

AU - Kwon, Jeannie K.

AU - Otero, Hansel J.

AU - Sher, Andrew C.

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AU - Taragin, Benjamin H.

AU - Towbin, Alexander J.

AU - Wald, Christoph

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ER -

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

Abstract

UN SDGs

ASJC Scopus subject areas

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