Abstract: Medical imaging large-scale models demonstrate broad application prospects in the field of tumor diagnosis and treatment. Their powerful high-dimensional feature extraction and data analysis capabilities have brought revolutionary breakthroughs to precision oncology, driving the transformation of tumor diagnosis and treatment paradigms. However, current research in this field still faces numerous challenges and technical bottlenecks. Based on the research background of artificial intelligence (AI) large-scale models, this article systematically summarizes the current applications of medical imaging large-scale models in the medical field from four key dimensions:construction of large-scale medical imaging datasets, optimization of large-scale model algorithms, computational resource requirements, and clinical application scenarios. Furthermore, it provides a forwardlooking perspective on the future development directions of medical imaging large-scale models in precision oncology, aiming to offer practical guidance for advancing tumor diagnosis and treatment.