AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-rays

The AdFair-CLIP model has been developed to address fairness concerns in Contrastive Language-Image Pre-training (CLIP) specifically for chest X-rays. While CLIP models have demonstrated strong performance in medical image classification, they have been found to often overlook demographic biases, which can result in disparities in diagnostic outcomes. This research underscores the critical importance of ensuring that such models maintain reliability and fairness across different demographic groups. By focusing on mitigating these biases, AdFair-CLIP aims to improve equitable diagnostic accuracy in medical imaging. The work highlights a growing recognition within the AI and medical communities of the need to address fairness as a fundamental component of model development. This approach aligns with broader efforts to enhance the trustworthiness and inclusivity of AI applications in healthcare. Overall, AdFair-CLIP represents a significant step toward more equitable AI-driven medical diagnostics.