Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries. However, with the development of data science, machine learning and deep learning techniques, this right is usually neglected or violated as more and more patients' data are being collected and used for model training, especially in intelligent healthcare, thus making intelligent healthcare a sector where technology must meet the law, regulations, and privacy principles to ensure that the innovation is for the common good. In order to secure patients' right to be forgotten, we proposed a novel solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing a new approach called knowledge purification. To implement our solution, we developed AFS, a unified open-source software, which is able to evaluate and revoke patients' private data from pre-trained deep learning models. We demonstrated the generality of AFS by applying it to four tasks on different datasets with various data sizes and architectures of deep learning networks. The software is publicly available at \url{https://github.com/JoshuaChou2018/AFS}.

翻译：撤销个人隐私数据是基本人权之一，已受到多国隐私保护法律的保障。然而，随着数据科学、机器学习和深度学习技术的发展，这一权利常被忽视或侵犯——尤其在智能医疗领域，越来越多的患者数据被收集用于模型训练，使得该领域成为技术必须与法律、法规和隐私原则相协调以确保创新惠及公众的关键领域。为保障患者的被遗忘权，我们提出了一种创新解决方案：通过审计引导遗忘过程（审计指判定数据集是否用于模型训练，遗忘则要求目标模型遗忘查询数据集的信息）。我们引入名为"知识净化"的新方法统一上述两个任务。为实现该方案，我们开发了AFS统一开源软件，能从预训练的深度学习模型中评估并撤销患者隐私数据。通过将AFS应用于四种不同数据规模与网络架构的数据集任务，验证了其通用性。该软件已在\url{https://github.com/JoshuaChou2018/AFS}开源。