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 diagnostic and therapeutic 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 reviews the current research status of medical imaging large-scale models from three key dimensions: the construction of large-scale medical imaging datasets, optimization of large-scale model algorithms, and computational resource requirements. Furthermore, it elaborates on the application scenarios of these models in precision oncology and provides a forward-looking perspective on their future development. The aim is to offer practical guidance for advancing precision diagnosis and treatment of tumors.