MultiFair: Multimodal Balanced Fairness-Aware Medical Classification with Dual-Level Gradient Modulation

This paper presents MultiFair, a method for improving fairness and performance in medical image classification by using multimodal data and dual-level gradient modulation to reduce bias across demographic groups. The approach addresses a critical challenge in regulated healthcare AI: ensuring diagnostic models perform equitably for all patient populations while maintaining clinical accuracy.

This paper presents MultiFair, a method for improving fairness and performance in medical image classification by using multimodal data and dual-level gradient modulation to reduce bias across demographic groups. The approach addresses a critical challenge in regulated healthcare AI: ensuring diagnostic models perform equitably for all patient populations while maintaining clinical accuracy. The technique is particularly relevant for enterprise healthcare organizations deploying AI systems that must comply with fairness and non-discrimination standards.

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