Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms

Understand

An increasing number of reports raise concerns about the risk that machine learning algorithms could amplify health disparities due to biases embedded in the training data.

• Seyyed-Kalantari et al.
• find that models trained on three chest X-ray datasets yield disparities in false-positive rates (FPR) across subgroups on the 'no-finding' label (indicating the absence of disease).
• The models consistently yield higher FPR on subgroups known to be historically underserved, and the study concludes that the models exhibit and potentially even amplify systematic underdiagnosis.