AI models for tasks such as pathology and dermatology struggle to generalize to new patient groups or hospitals that they were not trained on; learning more robust features from unlabeled data could prevent overfitting to the training distribution and thereby increase fairness.

中文翻译：

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利用无标签数据增强医疗AI的公平性

用于病理学和皮肤病学等任务的人工智能模型很难推广到未经培训的新患者群体或医院；从未标记的数据中学习更强大的特征可以防止过度拟合训练分布，从而提高公平性。