Lack of children in public medical imaging data points to growing age bias in biomedical AI

Abstract

This study highlights the urgent need for increased pediatric representation in publicly accessible medical datasets. Our findings suggest that the lack of pediatric data may contribute to the scarcity of AI tools for children and the poor performance of adult-trained models in this population. We provide actionable recommendations for researchers, policymakers, and data curators to address this age equity gap and mitigate the potential harms of AI systems not trained on pediatric patients.

Ähnliche Arbeiten