In tasks involving the interpretation of medical images, suitably trained machine-learning models often exceed the performance of medical experts. Yet such a high-level of performance typically requires that the models be trained with relevant datasets that have been painstakingly annotated by experts. Here we show that a self-supervised model trained on chest X-ray images that lack explicit annotations performs pathology-classification tasks with accuracies comparable to those of radiologists. On an external validation dataset of chest X-rays, the self-supervised model outperformed a fully supervised model in the detection of three pathologies (out of eight), and the performance generalized to pathologies that were not explicitly annotated for model training, to multiple image-interpretation tasks and to datasets from multiple institutions.

在涉及医学图像解释的任务中，经过适当训练的机器学习模型通常会超过医学专家的表现。然而，如此高水平的性能通常需要使用专家精心注释的相关数据集对模型进行训练。在这里，我们展示了一个在缺乏明确注释的胸部 X 射线图像上训练的自我监督模型执行病理分类任务，其准确度与放射科医生的准确度相当。在胸部 X 光片的外部验证数据集上，自监督模型在检测三种病理（八种）方面优于完全监督模型，并且性能推广到未明确注释模型训练的病理，多图像解释任务和来自多个机构的数据集。