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Explanation-guided learning can significantly boost the robustness of medical NER, achieving consistent performance gains across multiple models.
IViT achieves 93.80% accuracy in skin disease detection while reducing feature redundancy by 29.5%, striking a crucial balance between interpretability and performance.
