Radiation Oncology
Radiation oncology aims to deliver precise radiation doses to cancerous tumors while minimizing damage to surrounding healthy tissues. Current research heavily emphasizes the use of artificial intelligence, particularly deep learning models like transformers and convolutional neural networks, to automate tasks such as treatment planning, organ segmentation, and motion tracking during radiotherapy. These advancements leverage large datasets and advanced algorithms to improve treatment accuracy, efficiency, and ultimately, patient outcomes. The integration of large language models is also showing promise in structuring unstructured clinical data and assisting with treatment decision-making.
Papers
An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncology
Reza Khanmohammadi, Mohammad M. Ghassemi, Kyle Verdecchia, Ahmed I. Ghanem, Luo Bing, Indrin J. Chetty, Hassan Bagher-Ebadian, Farzan Siddiqui, Mohamed Elshaikh, Benjamin Movsas, Kundan Thind
LLM-driven Multimodal Target Volume Contouring in Radiation Oncology
Yujin Oh, Sangjoon Park, Hwa Kyung Byun, Yeona Cho, Ik Jae Lee, Jin Sung Kim, Jong Chul Ye