5 barriers to AI adoption in pediatric cancer imaging

There are five key barriers that are preventing the proliferation of artificial intelligence in pediatric cancer imaging, according to a recently published editorial. 

The excitement surrounding AI in adult oncology is “palpable,” experts note, with deep learning models supporting everything from lesion detection to clinical trial enrollment. 

“Yet pediatric oncology remains on the periphery of this revolution as the translation of AI from adult success stories to children is obstructed by unique and persistent barriers,” Alexander J. Towbin, MD, Cincinnati Children’s, and Amit Gupta, MD, with the All India Institute of Medical Sciences, New Delhi, wrote Feb. 16 in Cancer Imaging. 

The two offered five factors they believe are holding back AI in children’s cancer care: 

1. Simple epidemiology: Pediatric cancers are rare, accounting for 1% of new diagnoses, and tumor types are even more uncommon. “Such scarcity limits the quantity of imaging data available for AI training and validation, creating a fundamental mismatch between the scale of pediatric oncology and the data appetite of contemporary deep learning systems.” 

2. Fragmentation of available data: These cases are scattered across hundreds of specialized cancer centers, with over 200 such institutions managing the thousands of instances in the U.S. “Without systematic data sharing, models remain narrow reflections of local populations, unable to generalize across institutions or imaging platforms.”