Bias remains a major barrier to the clinical adoption of AI in dermatology, as diagnostic models underperform on darker skin tones. We present LesionTABE, a fairness-centric framework that couples adversarial debiasing with dermatology-specific foundation model embeddings. Evaluated across multiple datasets covering both malignant and inflammatory conditions, LesionTABE achieves over a 25\% improvement in fairness metrics compared to a ResNet-152 baseline, outperforming existing debiasing methods while simultaneously enhancing overall diagnostic accuracy. These results highlight the potential of foundation model debiasing as a step towards equitable clinical AI adoption.

翻译：在皮肤病学领域，人工智能的临床应用中，偏见仍然是一个主要障碍，因为诊断模型在较深肤色上的表现不佳。我们提出了LesionTABE，这是一个以公平性为中心的框架，它将对抗性去偏与皮肤病学专用基础模型嵌入相结合。在涵盖恶性和炎症性病变的多个数据集上进行评估后，与ResNet-152基线相比，LesionTABE在公平性指标上实现了超过25%的提升，其性能优于现有的去偏方法，同时提高了整体诊断准确性。这些结果凸显了基础模型去偏作为迈向公平临床人工智能应用的一步所具有的潜力。