Deep Reinforcement Learning in Oncology: Advances in Cancer Imaging, Radiotherapy, and Personalized Treatment
Year: 2026
Type: article
Abstract: Reinforcement Learning (RL) enables computers to learn dynamic decision making methods via trial and error, to maximize numerical reward signals. Its roots lie in early work across many fields, but recent advances have improved its capabilities greatly. Cancer is complex and dynamic and hence RL has a lot of potential in the field of oncology especially in imaging where it can improve accuracy and efficiency . In this study, we review RL in cancer and briefly introduce RL algorithms and categories. It then discusses applications of RL in cancer including in imaging and radiation. The report concludes with current challenges and future perspectives, emphasizing RL’s promise to personalize cancer diagnosis and therapy.
Source: Research
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7165391549