Current challenges for global equity related to the implementation of artificial intelligence in pediatric imaging.
Authors
Affiliations (7)
Affiliations (7)
- Department of Pediatric Radiology & Nuclear Medicine, University Medical Center Utrecht/Wilhelmina Children's Hospital, P.O. Box 85500, 3508 GA, Utrecht, Netherlands. [email protected].
- Department of Diagnostic and Interventional Oncoradiology, All India Institute of Medical Sciences, New Delhi, India.
- Department of Pediatric Radiology, Musgrove Park Hospital, University of Bristol, Bristol, UK.
- Department of Radiology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Paediatric Diagnostic Imaging, Dr. J. Naidoo Inc., Johannesburg, South Africa.
- Envisionit Deep AI Ltd, Great Ormond Street Hospital, Cobham, UK.
- Edward B. Singleton Department of Radiology, Texas Children's Hospital, Baylor College of Medicine, Houston, USA.
Abstract
Artificial intelligence (AI) is rapidly transforming medical imaging, offering unprecedented opportunities to enhance diagnostic accuracy, streamline workflows, and personalize care. However, its integration into pediatric radiology presents unique challenges that threaten to widen existing global health disparities if not addressed thoughtfully. These challenges are related to data inequality and bias in model development, infrastructure disparities, regulatory and ethical gaps, workforce capacity and training gaps, language and localization barriers, costs and commercialization, and sustainability and long-term support issues. This article, written by representatives from the World Federation of Pediatric Imaging (WFPI), will address the key barriers to global validation and implementation of AI in pediatric radiology and how they can be addressed. By linking these domains to practical actions and responsibilities and outlining a time-sequenced roadmap, this paper provides an equity-focused, pediatric-specific framework to guide global implementation.