Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives

2025-02-24

AI Summary

Foundation models in medical image analysis exhibit significant fairness gaps when deployed in resource-constrained regions and underserved populations. Method: We propose the first end-to-end fairness framework spanning data acquisition, self-supervised pretraining, fine-tuning, and clinical deployment—moving beyond model-centric bias mitigation to integrate structured data governance, multidimensional bias assessment and mitigation protocols, and cross-institutional policy and deployment coordination. Contribution/Results: The framework yields a reproducible, practice-oriented fairness guideline that substantially improves model robustness and clinical validity across diverse demographic subgroups and low-resource settings. It advances methodological rigor and practical implementation pathways for equitable AI-driven healthcare globally.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies

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.

Problem

• Ensuring equitable AI in healthcare
• Mitigating bias in Foundation Models
• Democratizing advanced healthcare technologies

Innovation

• Foundation Models for fairness
• Self-supervised learning adaptation
• Integrated bias mitigation framework