Chest X-ray (CXR) classification faces challenges from long-tailed, multi-label data distributions and demographic biases in medical AI systems. To address these, we present LTCXNet – a framework combining ConvNeXt, ML-Decoder, and multi-branch learning – evaluated on Pruned MIMIC-CXR-LT dataset curated for long-tail scenarios. The model achieves large performance gains especially in rare classes, with 79% and 48% improvements in detecting Pneumoperitoneum and Pneumomediastinum respectively. We introduce “mAUCr” fairness metric to quantify racial group performance disparities, demonstrating LTCXNet’s superior fairness in tail class subgroups compared to existing long-tail methods. This work advances medical imaging analysis by addressing both class imbalance and demographic bias through novel architectural integration and evaluation metrics. Our code is available on code .