The broad application of artificial intelligence techniques in medicine is currently hindered by limited dataset availability for algorithm training and validation, due to the absence of standardized electronic medical records, and strict legal and ethical requirements to protect patient privacy. In medical imaging, harmonized data exchange formats such as Digital Imaging and Communication in Medicine and electronic data storage are the standard, partially addressing the first issue, but the requirements for privacy preservation are equally strict. To prevent patient privacy compromise while promoting scientific research on large datasets that aims to improve patient care, the implementation of technical solutions to simultaneously address the demands for data protection and utilization is mandatory. Here we present an overview of current and next-generation methods for federated, secure and privacy-preserving artificial intelligence with a focus on medical imaging applications, alongside potential attack vectors and future prospects in medical imaging and beyond.

由于缺乏标准化的电子病历，以及用于保护患者隐私的严格法律和道德要求，目前人工智能技术在医学中的广泛应用受到算法训练和验证数据集可用性的限制。在医学成像中，统一的数据交换格式（例如医学中的数字成像和通信）和电子数据存储是标准，部分解决了第一个问题，但是对隐私保护的要求也同样严格。为了防止损害患者隐私，同时促进旨在改善患者护理的大型数据集的科学研究，必须实施技术解决方案以同时满足对数据保护和利用的需求。