Quality assurance of artificial intelligence models applied to case-specific radiotherapy
10.3760/cma.j.cn113030-20231121-00170
- VernacularTitle:放疗人工智能模型应用于特定病例的质量保证
- Author:
Xiaonan LIU
1
;
Guodong JIN
;
Wenyu WANG
;
Ji ZHU
;
Bining YANG
;
Siqi YUAN
;
Hong QUAN
;
Kuo MEN
;
Jianrong DAI
Author Information
1. 国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院放疗科，北京 100021
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Quality assurance;
Image registration;
Image generation;
Region of interest segmentation;
Treatment planning
- From:
Chinese Journal of Radiation Oncology
2025;34(9):949-953
- CountryChina
- Language:Chinese
-
Abstract:
Artificial intelligence (AI) technologies are being widely applied in radiotherapy. However, the integration of AI into clinical workflows of radiotherapy faces a series of challenges, such as poor model interpretability, domain shifts between clinical application and training data, and the inherent model uncertainties. Therefore, case-specific quality assurance (QA) is essential before deploying AI models in clinical practice. This paper reviews and summarizes QA methodologies for the application of AI models in radiotherapy across four key areas: image registration, image generation, region of interest segmentation, and treatment planning.
