A Mathematically Rigorous Framework for Mitigating Annotation Bias Across Fitzpatrick Skin Types in Dermatology AI

Deep learning models in computational dermatology frequently exhibit reduced diagnostic performance on darker skin tones (Fitzpatrick skin types IV–VI) because of annotation bias, under-representation of darker skin images, and variability in annotator expertise. This paper presents a mathematically grounded framework that aims to mitigate these challenges through three complementary components: (1) a conditional generative adversarial network (cGAN) with structural texture constraints for generating clinically realistic synthetic anchor images, (2) a skin-invariant deep metric learning strategy for separating pathological features from skin-tone characteristics, and (3) a variational Bayesian adjudication model for estimating annotator reliability across demographic groups. Rather than claiming validated clinical performance, this work provides a conceptual and mathematical foundation for developing fairer dermatology AI systems. Future validation using diverse clinical datasets and expert dermatologist assessment will be necessary to establish the framework's effectiveness in real-world healthcare environments.

Dermatology AI, Annotation Bias, Fitzpatrick Skin Types, Generative Adversarial Networks, Metric Learning, Bayesian Adjudication, Global Health Equity

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