Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives

TL;DR

This paper reviews the challenges and perspectives of developing fair foundation models for medical image analysis, emphasizing the need for systematic bias mitigation throughout the development pipeline to promote equitable healthcare.

Contribution

It highlights the importance of integrated bias mitigation strategies across all stages of foundation model development for equitable medical AI.

Findings

Bias mitigation requires interventions from data collection to deployment.

Systematic bias mitigation improves fairness across demographic groups.

Policy engagement is crucial for addressing institutional barriers.

Abstract

Ensuring equitable Artificial Intelligence (AI) in healthcare demands systems that make unbiased decisions across all demographic groups, bridging technical innovation with ethical principles. Foundation Models (FMs), trained on vast datasets through self-supervised learning, enable efficient adaptation across medical imaging tasks while reducing dependency on labeled data. These models demonstrate potential for enhancing fairness, though significant challenges remain in achieving consistent performance across demographic groups. Our review indicates that effective bias mitigation in FMs requires systematic interventions throughout all stages of development. While previous approaches focused primarily on model-level bias mitigation, our analysis reveals that fairness in FMs requires integrated interventions throughout the development pipeline, from data documentation to deployment protocols. This comprehensive framework advances current knowledge by demonstrating how systematic bias mitigation, combined with policy engagement, can effectively address both technical and institutional barriers to equitable AI in healthcare. The development of equitable FMs represents a critical step toward democratizing advanced healthcare technologies, particularly for underserved populations and regions with limited medical infrastructure and computational resources.