Demographic‐aware deep learning for multi‐organ segmentation: Mitigating gender and age biases in CT images

In this work, we propose DA-Net, a segmentation network that explicitly incorporates age and gender attributes to mitigate performance disparities between pediatric and adult groups while combining multiple views of anatomic features to improve performance. By leveraging demographic information, DA-Net enhances segmentation accuracy, especially for gender-specific organs. The proposed framework highlights the necessity of developing fair and personalized models tailored to clinical applications, providing a foundation for building more equitable artificial intelligence systems in medical imaging.