Kaidar-Person Orit, Pfob André, Valentini Vincenzo, Aznar Marianne, Dekker Andre, Meattini Icro, de Boniface Jana, Krug David, Cardoso Maria Joao, Curigliano Giuseppe, Dubsky Peter, Poortmans Philip
Breast Radiation Unit, Sheba Tel Hashomer, Ramat Gan, Israel; Gray School of Medical Sciences, Tel-Aviv University, Tel-Aviv, Israel.
Breast Center Heidelberg, Hospital St. Elisabeth, Heidelberg, Germany; Department of Obstetrics & Gynecology, Heidelberg University Hospital, Heidelberg, Germany.
Breast. 2025 Jul 15;83:104537. doi: 10.1016/j.breast.2025.104537.
Artificial intelligence (AI) is being incorporated in several breast cancer care domains, including for radiation therapy (RT). Herein we provide a review about AI for the management and planning of RT for breast cancer, which is part of the Toolbox-3 project's multidisciplinary Delphi study, including a literature review of studies related to the topic raised by the Delphi questionnaire. Our review shows that available evidence mainly consists of small single institutional studies, often at least partly supported by commercial companies. Current studies suffer from a lack of transparency regarding how these systems were developed, the information they are based on, the algorithms used, and potential proprietary issues. This review provides a critical inter- and multidisciplinary assessment of existing systems to help us in guiding development and utilisation of AI-based tools in the field of radiation oncology. As medical professional users, we must remain vigilant and continue to improve our personal experience and knowledge that serves as the "ground truth". Employing AI required a critical mindset, particularly in medical applications which may influence the lives of our patients.
人工智能（AI）正被应用于多个乳腺癌护理领域，包括放射治疗（RT）。在此，我们对用于乳腺癌放疗管理和计划的人工智能进行综述，这是Toolbox-3项目多学科德尔菲研究的一部分，包括对与德尔菲问卷提出的主题相关研究的文献综述。我们的综述表明，现有证据主要由小型单机构研究组成，这些研究往往至少部分得到商业公司的支持。当前的研究在这些系统的开发方式、所基于的信息、使用的算法以及潜在的专有问题方面缺乏透明度。本综述对现有系统进行了批判性的跨学科和多学科评估，以帮助我们指导放射肿瘤学领域基于人工智能的工具的开发和应用。作为医学专业用户，我们必须保持警惕，不断提升作为“基本事实”的个人经验和知识。应用人工智能需要批判性思维，尤其是在可能影响患者生活的医学应用中。
