Optimizing the delivery of radiotherapy with artificial intelligence.
Authors
Affiliations (3)
Affiliations (3)
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
- Department of Radiation Oncology, Dartmouth Hitchcock Medical Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. [email protected].
Abstract
Artificial intelligence (AI) and machine learning are transformative technologies that have sparked both excitement and concern. In radiation oncology, AI has been successfully applied to automate tasks, such as auto-contouring, geometric adaptation, calculation of radiation dose and quality assurance, and predicting outcomes to provide optimal patient care. The clinical deployment of AI-driven and machine learning-based tools, however, continues to lag behind their perceived potentials, owing to a range of technical, practical, ethical and legal concerns. The original predictive AI algorithms have been expanded with technologies such as generative AI and foundation models to enable new applications, such as automated treatment planning and synthetic computed tomography generation, as well as improved prediction of individual patient outcomes. The increasing availability of innovations such as agentic AI, digital twins and multimodal AI could further improve the delivery and, thus, the efficacy of radiotherapy. In this Review, we present examples of successful AI applications in radiation oncology, provide an overview of the current challenges for implementing such tools in this specialty and strategies for addressing them, and discuss the broader implications of these tools in optimizing treatment outcomes and the quality of patient care.