  There has been significant progress in implementing deep learning models in
disease diagnosis using chest X- rays. Despite these advancements, inherent
biases in these models can lead to disparities in prediction accuracy across
protected groups. In this study, we propose a framework to achieve accurate
diagnostic outcomes and ensure fairness across intersectional groups in
high-dimensional chest X- ray multi-label classification. Transcending
traditional protected attributes, we consider complex interactions within
social determinants, enabling a more granular benchmark and evaluation of
fairness. We present a simple and robust method that involves retraining the
last classification layer of pre-trained models using a balanced dataset across
groups. Additionally, we account for fairness constraints and integrate
class-balanced fine-tuning for multi-label settings. The evaluation of our
method on the MIMIC-CXR dataset demonstrates that our framework achieves an
optimal tradeoff between accuracy and fairness compared to baseline methods.
