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.
Artificial intelligence in breast cancer radiotherapy: Insights from the Toolbox Consortium Delphi study / Kaidar-Person O.; Pfob A.; Valentini V.; Aznar M.; Dekker A.; Meattini I.; de Boniface J.; Krug D.; Cardoso M.J.; Curigliano G.; Dubsky P.; Poortmans P.. - In: THE BREAST. - ISSN 0960-9776. - ELETTRONICO. - 83:(2025), pp. 104537.0-104537.0. [10.1016/j.breast.2025.104537]
Artificial intelligence in breast cancer radiotherapy: Insights from the Toolbox Consortium Delphi study
Meattini I.;
2025
Abstract
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.
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