  The proliferation of artificial intelligence (AI) in radiology has shed light
on the risk of deep learning (DL) models exacerbating clinical biases towards
vulnerable patient populations. While prior literature has focused on
quantifying biases exhibited by trained DL models, demographically targeted
adversarial bias attacks on DL models and its implication in the clinical
environment remains an underexplored field of research in medical imaging. In
this work, we demonstrate that demographically targeted label poisoning attacks
can introduce undetectable underdiagnosis bias in DL models. Our results across
multiple performance metrics and demographic groups like sex, age, and their
intersectional subgroups show that adversarial bias attacks demonstrate
high-selectivity for bias in the targeted group by degrading group model
performance without impacting overall model performance. Furthermore, our
results indicate that adversarial bias attacks result in biased DL models that
propagate prediction bias even when evaluated with external datasets.
