放射治疗的治疗计划在过去几十年取得了巨大进步。这些进步以创新的硬件和算法的形式呈现，使强度调控放射治疗和容积调控弧线治疗等治疗方法得以应用，极大地改善了病人的治疗效果和生活质量。虽然这些发展改善了整体治疗质量，但也导致了治疗计划复杂性的增加。这导致了治疗计划时间的增加和最终批准的治疗计划质量的更高变异性。作为一个已经在技术上领先的领域，放射肿瘤学在使用人工智能方面进行了大量研究和实施。该领域已经开始展示使用这些技术在诊断、成像、分割、治疗计划、质量保证、治疗交付和随访等许多子领域中的功效。一些人工智能技术已经通过商业系统在临床上实施。在本文中，我们将概述放射治疗中涉及的治疗计划方法。特别地，我们将回顾最近涉及治疗计划流程自动化的研究和文献，这有望提高效率和治疗计划的质量。我们将随后介绍当前和未来的挑战，以及一些未来的展望。版权所有©2022 Elsevier Inc.保留所有权利。

Treatment planning in radiation therapy has progressed enormously over the past several decades. Such advancements came in the form of innovative hardware and algorithms, giving rise to modalities such as intensity-modulated radiation therapy and volume modulated arc therapy, greatly improving patient outcome and quality of life. While these developments have improved the overall plan quality, they have also given rise to higher treatment planning complexity. This has resulted in increased treatment planning time and higher variability in the final approved plan quality. Radiation oncology, as an already technologically advanced field, has much research and implementation involving the use of AI. The field has begun to show the efficacy of using such technologies in many of its sub-areas, such as in diagnosis, imaging, segmentation, treatment planning, quality assurance, treatment delivery, and follow-up. Some AI technologies have already been clinically implemented by commercial systems. In this article, we will provide an overview to methods involved with treatment planning in radiation therapy. In particular, we will review the recent research and literature related to automation of the treatment planning process, leading to potentially higher efficiency and higher quality plans. We will then present the current and future challenges, as well as some future perspectives.Copyright © 2022 Elsevier Inc. All rights reserved.