Underrepresentation of children in public medical imaging datasets

Abstract

Abstract Artificial intelligence (AI) has the potential to transform healthcare for all patients. Yet, there are disproportionately fewer paediatric AI studies and US Food and Drug Administration approvals relative to adults, indicating less effort focused on AI for children. Here, given that innovations in medical AI are accelerated by community-driven research on public datasets, we hypothesized that the disparity in AI for paediatrics is tied to the lack of public paediatric medical imaging data to support their development and evaluation. To that end, we systematically identified and reviewed 203 datasets, revealing that 33% of datasets lacked metadata on patient ages, and when available, children represented less than 2% of patients. To illustrate how a lack of paediatric data can lead to harmful algorithmic bias, we trained models to classify adult cardiomegaly and evaluated them on healthy children. We found a consistent pattern of age-related bias, reproducible across four large-scale public chest X-ray datasets. These findings suggest that the lack of public paediatric data hinders the development of safe AI for children, producing a landscape of adult-first and adult-only AI models with unknown patterns of bias in children.

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