MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis

AI systems analyzing medical images can perpetuate health disparities: Traditional machine learning methods for analyzing pathology images often fail to account for patient demographics (age, gender, race), which can cause the AI to learn spurious patterns that disadvantage certain populations—a critical barrier to clinical adoption.

Causal reasoning improves both fairness and accuracy: MeCaMIL uses structured causal graphs to distinguish disease signals from demographic correlations, achieving state-of-the-art diagnostic accuracy (93.9% on CAMELYON16, 97.7% on multi-cancer TCGA data) while simultaneously reducing demographic disparities by over 65% compared to existing methods.

Interpretability and fairness are interconnected: By making the reasoning process transparent through causal modeling rather than relying on "black box" attention mechanisms, the framework enables clinicians to understand why the AI makes decisions and whether those decisions are biased—essential for trustworthy clinical deployment.

Fairness improvements benefit underserved populations most: The framework shows particularly notable fairness gains for historically underrepresented groups in medical AI, suggesting that principled causal approaches can help address existing healthcare inequities rather than amplify them.

Responsible AI design requires explicit demographic consideration: Rather than treating patient demographics as optional auxiliary information, MeCaMIL demonstrates that deliberately modeling these factors through causal inference is necessary to build diagnostic systems that perform equitably across diverse populations.