Health Equity in Pediatric AI: New JACR White Paper Offers Solutions

The physiology, anatomy and diagnoses of pediatric patients vary widely from their adult counterparts, yet only three percent of the >200 AI Software as a Medical Device (SaMD) cleared by the Food and Drug Administration (FDA) that are currently listed in our online catalog indicate they are intended for pediatric use. How should this health equity gap be addressed? 

The American College of Radiology® (ACR®) Pediatric AI Workgroup recently published a White Paper in the Journal of American College of Radiology® (JACR®) to educate the radiology community about the health equity issue of lack of pediatric artificial intelligence (AI), improve the understanding of relevant pediatric AI issues, and offer solutions to address the inadequacies in pediatric AI development.   

The paper, titled “Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, A White Paper from the ACR Pediatric AI Workgroup,” stresses the importance of ensuring AI in radiology is safe, reliable and effective for children. 

The ACR Pediatric AI Workgroup suggests addressing this current unmet need in several ways: 

  • Existing adult AI algorithms should be assessed in pediatric cohorts prior to potential use in children
  • Vendors could be incentivized to develop AI using suitable pediatric data, either in separate pediatric models or in combined adult and pediatric datasets.   
  • Regulatory bodies could encourage transparency about the inclusion of pediatric patients in AI datasets to ensure correct application of AI based on customary metrics in pediatric care such as age.    

The Workgroup, within the ACR Informatics Commission, is also sponsoring the Image IntelliGently™ campaign to raise awareness of the need for improved implementation and governance over pediatric AI. The group is charged, through stakeholder consensus, with providing guidance to ensure that all pediatric patients will have equitable access to clinically meaningful AI as it becomes increasingly available for use in adults.  

This White Paper was authored by ACR Pediatric AI Workgroup members: Marla B.K. Sammer, MD, MHA; Yasmin S. Akbari, MD; Richard A. Barth, MD, FACR, FAAP; Steven L. Blumer, MD, MBA, CPE, FAAP; Jonathan R. Dillman, MD, MSc, FACR, FSAR; Shannon G. Farmakis, MD, FAAP; Don Frush, MD, FACR, FAAP FSABI; Ami Gokli, MD; Safwan Halabi, MD; Ramesh Iyer, MD; Aparna Joshi, MD, FAAP; Jeannie K. Kwon, MD; Hansel Otero, MD, FAAP; Adrew C. Sher, MD; Susan Sotardi, MD; Benjamin H. Targin, MD, FACR; Alexander J. Towbin, MD, FACR, FAAP; and Christoph Wald, MD, PhD, MBA, FACR.

For more information, contact the ACR Data Science Institute™ at