人工智能（AI）在放射治疗领域具有异常潜力，但需要大量策划的数据集（通常涉及成像数据和相应注释），才能开发放射治疗AI模型。重要的是，最近建立了科学数据管理可找到性、可访问性、互操作性、可重用性（FAIR）原则，使越来越多与放射治疗相关的数据集通过数据库进行传播，从而成为AI模型建立的丰富数据源。本文回顾了放射治疗数据传播的现状和未来，特别强调已发表的成像数据集、AI数据挑战和相关基础设施。此外，我们提供了FAIR数据传播协议的历史背景、当前放射治疗数据分发的困难以及有关数据传播的建议，希望最终利用于AI模型。通过FAIR原则和标准化的数据传播方法，放射治疗AI研究没有丝毫损失，却会获得很大收益。版权所有 © 2022作者。由Elsevier Inc.出版，保留所有权利。

Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data repositories, thereby acting as a rich source of data for AI model building. This manuscript reviews the current and future state of radiation oncology data dissemination, with a particular emphasis on published imaging datasets, AI data challenges, and associated infrastructure. Moreover, we provide historical context of FAIR data dissemination protocols, difficulties in the current distribution of radiation oncology data, and recommendations regarding data dissemination for eventual utilization in AI models. Through FAIR principles and standardized approaches to data dissemination, radiation oncology AI research has nothing to lose and everything to gain.Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.