Abstract
Low-dose radiation therapy has emerged as a promising modality for cancer treatment because of its ability to stimulate antitumor immune responses while minimizing damage to healthy tissues. However, the significant heterogeneity in immune responses among patients complicates its clinical application, hindering outcome prediction and treatment personalization. Artificial intelligence (AI) offers a transformative solution by integrating multidimensional data such as immunomics, radiomics, and clinical features to decode complex immune patterns and predict individual therapeutic outcomes. This editorial explored the potential of AI to address immune response heterogeneity in low-dose radiation therapy and proposed an AI-driven framework for precision immunotherapy. While promising, challenges, including data standardization, model interpretability, and clinical validation, must be overcome to ensure successful integration into oncological practice.