Artificial intelligence (AI) and deep learning techniques have gained significant attraction in recent years, owing to their remarkable capability of achieving high performance across a broad range of applications. However, a crucial challenge in training such models is the acquisition of vast amounts of data, which is often limited in fields like healthcare. In this domain, medical data is typically scattered across various sources such as hospitals, clinics, and wearable devices. The aggregated data collected from multiple sources in the healthcare domain is sufficient for training advanced deep learning models. However, these sources are frequently hesitant to share such data due to privacy considerations. To address this challenge, researchers have proposed the integration of blockchain and federated learning to develop a system that facilitates the secure sharing of medical records. This work provides a succinct review of the current state of the art in the use of blockchain and federated learning in the decentralized healthcare domain.

翻译：人工智能（AI）和深度学习技术近年来因其在广泛应用中展现出的卓越性能而备受瞩目。然而，训练此类模型的关键挑战在于获取海量数据，这在医疗保健等领域往往受到限制。在该领域中，医疗数据通常分散于医院、诊所和可穿戴设备等多种来源。从医疗领域多源聚合的数据足以训练先进的深度学习模型。然而，出于隐私考量，这些数据源常常不愿共享此类数据。为应对这一挑战，研究人员提出了结合区块链与联邦学习的方法，以构建一个促进医疗记录安全共享的系统。本文对当前去中心化医疗领域中区块链及联邦学习应用的最新研究进展进行了简明综述。