Artificial Intelligence Approaches in Precision Radiotherapy_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

WANG Qiang ^1,2 , XIAO Qing ¹ , BAI Long ¹ , SONG Ying ¹ , BAI Sen ¹ , ZHANG Yi ² ,  LI Guangjun ¹ ,  HU Junjie ²

1. Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. College of Computer Science, Sichuan University, Chengdu 610065, P.R. China;

Corresponding author：

LI Guangjun, Email: gjnick829@sina.com; HU Junjie, Email: hujunjie@scu.edu.cn

Keywords：

artificial intelligence; radiation therapy; image reconstruction; auto-segmentation; treatment planning

DOI：

10.7507/1007-9424.202506025

Video：

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Objective To systematically summarize recent advancements in the application of artificial intelligence (AI) in key components of radiotherapy, explore the integration of technical innovations with clinical practice, and identify current limitations in real-world implementation. Methods A comprehensive analysis of representative studies from recent years was conducted, focusing on the technical implementation and clinical effectiveness of AI in image reconstruction, automatic delineation of target volumes (TV) and organs at risk (OAR), intelligent treatment planning, and prediction of radiotherapy-related toxicities. Particular attention was given to deep learning models, multimodal data integration, and their roles in enhancing decision-making processes. Results AI-based low-dose image enhancement techniques had significantly improved image quality. Automated segmentation methods had increased the efficiency and consistency of contouring. Both knowledge-driven and data-driven planning systems had addressed the limitations of traditional experience-dependent approaches, contributing to higher quality and reproducibility in treatment plans. Additionally, toxicity prediction models that incorporate multimodal data enable more accurate, personalized risk assessment, supporting safer and more effective individualized radiotherapy. Conclusions Radiotherapy is a fundamental modality in cancer treatment. However, achieving precise tumor ablation while minimizing damage to surrounding healthy tissues remains a significant challenge. AI has demonstrated considerable value across multiple technical stages of radiotherapy, enhancing precision, efficiency, and personalization. Nevertheless, challenges such as limited model generalizability, lack of data standardization, and insufficient clinical validation persist. Future work should emphasize the alignment of algorithmic development with clinical demands to facilitate the standardized, reliable, and practical application of AI in radiotherapy.

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