Deep neural networks excel in medical imaging but remain prone to biases, leading to fairness gaps across demographic groups. We provide the first systematic exploration of Human-AI alignment and fairness in this domain. Our results show that incorporating human insights consistently reduces fairness gaps and enhances out-of-domain generalization, though excessive alignment can introduce performance trade-offs, emphasizing the need for calibrated strategies. These findings highlight Human-AI alignment as a promising approach for developing fair, robust, and generalizable medical AI systems, striking a balance between expert guidance and automated efficiency. Our code is available atthis https URL.
View on arXiv@article{luo2025_2505.10231,
title={ On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging },
author={ Haozhe Luo and Ziyu Zhou and Zixin Shu and Aurélie Pahud de Mortanges and Robert Berke and Mauricio Reyes },
journal={arXiv preprint arXiv:2505.10231},
year={ 2025 }
}