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常规放疗计划设计是一个耗时耗力的过程，需要在计划优化中不断调整参数来寻找最优计划。此外，计划设计者之间的经验差异、投入计划设计的时间以及医疗机构的执行标准都会影响计划质量，从而影响临床治疗效果以及患者预后。近年来自动计划发展迅速，自动计划能够在保证计划质量前提下提升计划设计效率。当前已有一些方法致力于放疗计划设计的自动化，如Eclipse和Pinnacle商用治疗计划系统中的Rapid Plan和Auto-Planning功能，也有研究将人工智能技术应用于剂量预测以实现自动计划。本文对现有的放疗自动计划研究做一综述，介绍各类自动计划方法的实现原理、临床效果以及存在的问题。“,”The design of a conventional radiotherapy plan is a time-consuming and labor-intensive process, and relevant parameters need to be continuously adjusted in the plan optimization to identify the optimal plan. In addition, experience differences between planners, time invested in plan design, and institutional standards all affect the quality of the plan, which in turn influences clinical outcomes and patient prognosis. In recent years, automatic planning has developed rapidly, which can improve the efficiency of planning design while ensuring the quality of the plan. At present, there are several methods dedicated to the automation of radiotherapy planning design, such as the Rapid Plan and Auto-Planning functions in Eclipse and Pinnacle commercial treatment planning systems, and there are also studies applying artificial intelligence technology in dose prediction to achieve automatic planning. In this article, the research progress on automatic radiotherapy planning was reviewed, and the realization principles, clinical efficacy and existing problems of various automatic planning methods were illustrated.