Bridging the experience gap in pediatric radiology: towards AI-assisted diagnosis for children

Excerpt

The interpretation of pediatric imaging is particularly challenging due to the ongoing development of the pediatric skeleton throughout childhood and the different fracture patterns encountered in children versus adults [1‐3]. Yet, a significant number of healthcare practices rely heavily on non-radiologists (emergency room physicians), radiology residents, fellows, or adult radiologists for interpretation of pediatric imaging exams, especially in settings requiring rapid turnaround times, such as the emergency department. In these critical scenarios, the promptness and precision of a diagnosis can profoundly influence patient outcomes. As the scarcity of experienced pediatric radiologists has led to persistent staffing challenges globally, AI assistance could be a means to help aid the diagnosis for certain pediatric imaging workflows that pose specific challenges. While many commercial radiology AI applications have focused primarily on adults, the number of pediatric AI applications from the research community is growing quickly and could pave the way for an AI assisted radiology workflow for children. …