Claessens Michaël, Oria Carmen Seller, Brouwer Charlotte L, Ziemer Benjamin P, Scholey Jessica E, Lin Hui, Witztum Alon, Morin Olivier, Naqa Issam El, Van Elmpt Wouter, Verellen Dirk
Faculty of Medicine and Health Sciences, Department of Radiation Oncology, Iridium Network, University of Antwerp, Belgium, Wilrijk (Antwerp), Belgium..
Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Semin Radiat Oncol. 2022 Oct;32(4):421-431. doi: 10.1016/j.semradonc.2022.06.011.
Recent advancements in artificial intelligence (AI) in the domain of radiation therapy (RT) and their integration into modern software-based systems raise new challenges to the profession of medical physics experts. These AI algorithms are typically data-driven, may be continuously evolving, and their behavior has a degree of (acceptable) uncertainty due to inherent noise in training data and the substantial number of parameters that are used in the algorithms. These characteristics request adaptive, and new comprehensive quality assurance (QA) approaches to guarantee the individual patient treatment quality during AI algorithm development and subsequent deployment in a clinical RT environment. However, the QA for AI-based systems is an emerging area, which has not been intensively explored and requires interactive collaborations between medical doctors, medical physics experts, and commercial/research AI institutions. This article summarizes the current QA methodologies for AI modules of every subdomain in RT with further focus on persistent shortcomings and upcoming key challenges and perspectives.
人工智能（AI）在放射治疗（RT）领域的最新进展以及它们与现代基于软件的系统的集成给医学物理专家这一职业带来了新的挑战。这些AI算法通常是数据驱动的，可能会不断发展，并且由于训练数据中的固有噪声以及算法中使用的大量参数，其行为具有一定程度的（可接受的）不确定性。这些特性要求采用适应性的、全新的全面质量保证（QA）方法，以确保在AI算法开发以及随后在临床RT环境中部署期间的个体患者治疗质量。然而，基于AI的系统的QA是一个新兴领域，尚未得到深入探索，需要医生、医学物理专家以及商业/研究AI机构之间的互动合作。本文总结了RT中每个子领域的AI模块当前的QA方法，进一步关注持续存在的缺点以及即将面临的关键挑战和前景。
