<ul><li>The rapid growth of healthcare data and advances in computational power have accelerated the adoption of artificial intelligence (AI) in medicine.</li><li>To address potential disparities in healthcare AI, a novel gradient reconciliation framework called FairGrad has been proposed.</li><li>FairGrad balances predictive performance and multi-attribute fairness optimization in healthcare AI models by projecting each gradient vector onto the orthogonal plane of the others.</li><li>FairGrad achieved statistically significant improvements in multi-attribute fairness metrics while maintaining competitive predictive accuracy in real-world healthcare datasets.</li></ul>

Balancing Fairness and Performance in Healthcare AI: A Gradient Reconciliation Approach

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