Artificial intelligence-powered innovations in radiotherapy: boosting efficiency and efficacy
Cancer remains a substantial global health challenge, with steadily increasing incidence rates. Radiotherapy (RT) is a crucial component in cancer treatment. Nevertheless, due to limited resources, there is an urgent need to enhance both its efficiency and therapeutic efficacy. The integration of Ar...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-02-01
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| Series: | Medical Review |
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| Online Access: | https://doi.org/10.1515/mr-2025-0007 |
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| _version_ | 1849225148476948480 |
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| author | Chen Junyi Zhu Xinlin Jin Jian-Yue Kong Feng-Ming (Spring) Yang Gen |
| author_facet | Chen Junyi Zhu Xinlin Jin Jian-Yue Kong Feng-Ming (Spring) Yang Gen |
| author_sort | Chen Junyi |
| collection | DOAJ |
| description | Cancer remains a substantial global health challenge, with steadily increasing incidence rates. Radiotherapy (RT) is a crucial component in cancer treatment. Nevertheless, due to limited resources, there is an urgent need to enhance both its efficiency and therapeutic efficacy. The integration of Artificial Intelligence (AI) into RT has proven to significantly improve treatment efficiency, especially in time-consuming tasks. This perspective demonstrates how AI enhances the efficiency of target delineation and treatment planning, and introduces the concept of All-in-One RT, which may greatly improve RT efficiency. Furthermore, the concept of Radiotherapy Digital Twins (RDTs) is introduced. By integrating patient-specific data with AI, RDTs enable personalized and precise treatment, as well as the evaluation of therapeutic efficacy. This perspective highlights the transformative impact of AI and digital twin technologies in revolutionizing cancer RT, with the aim of making RT more accessible and effective on a global scale. |
| format | Article |
| id | doaj-art-fdcd6bce547e4f4aa85aa1eabe07d34e |
| institution | Kabale University |
| issn | 2749-9642 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Medical Review |
| spelling | doaj-art-fdcd6bce547e4f4aa85aa1eabe07d34e2025-08-25T06:10:55ZengDe GruyterMedical Review2749-96422025-02-015434835110.1515/mr-2025-0007Artificial intelligence-powered innovations in radiotherapy: boosting efficiency and efficacyChen Junyi0Zhu Xinlin1Jin Jian-Yue2Kong Feng-Ming (Spring)3Yang Gen4State Key Laboratory of Nuclear Physics and Technology, School of Physics, 12465Peking University, Beijing, ChinaState Key Laboratory of Nuclear Physics and Technology, School of Physics, 12465Peking University, Beijing, ChinaSchool of Biomedical Engineering, Capital Medical University, Beijing, ChinaDepartment of Clinical Oncology, University of Hong Kong, Hong Kong, ChinaState Key Laboratory of Nuclear Physics and Technology, School of Physics, 12465Peking University, Beijing, ChinaCancer remains a substantial global health challenge, with steadily increasing incidence rates. Radiotherapy (RT) is a crucial component in cancer treatment. Nevertheless, due to limited resources, there is an urgent need to enhance both its efficiency and therapeutic efficacy. The integration of Artificial Intelligence (AI) into RT has proven to significantly improve treatment efficiency, especially in time-consuming tasks. This perspective demonstrates how AI enhances the efficiency of target delineation and treatment planning, and introduces the concept of All-in-One RT, which may greatly improve RT efficiency. Furthermore, the concept of Radiotherapy Digital Twins (RDTs) is introduced. By integrating patient-specific data with AI, RDTs enable personalized and precise treatment, as well as the evaluation of therapeutic efficacy. This perspective highlights the transformative impact of AI and digital twin technologies in revolutionizing cancer RT, with the aim of making RT more accessible and effective on a global scale.https://doi.org/10.1515/mr-2025-0007radiotherapyartificial intelligencedigital twins |
| spellingShingle | Chen Junyi Zhu Xinlin Jin Jian-Yue Kong Feng-Ming (Spring) Yang Gen Artificial intelligence-powered innovations in radiotherapy: boosting efficiency and efficacy Medical Review radiotherapy artificial intelligence digital twins |
| title | Artificial intelligence-powered innovations in radiotherapy: boosting efficiency and efficacy |
| title_full | Artificial intelligence-powered innovations in radiotherapy: boosting efficiency and efficacy |
| title_fullStr | Artificial intelligence-powered innovations in radiotherapy: boosting efficiency and efficacy |
| title_full_unstemmed | Artificial intelligence-powered innovations in radiotherapy: boosting efficiency and efficacy |
| title_short | Artificial intelligence-powered innovations in radiotherapy: boosting efficiency and efficacy |
| title_sort | artificial intelligence powered innovations in radiotherapy boosting efficiency and efficacy |
| topic | radiotherapy artificial intelligence digital twins |
| url | https://doi.org/10.1515/mr-2025-0007 |
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