Medical foundation models represent a significant application of large-scale pre-trained model technology in the healthcare domain and have become a key research focus in intelligent medical assistance. By leveraging pretraining on vast amounts of medical data， these models exhibit critical capabilities such as cross-task transfer， multimodal understanding， and complex reasoning， overcoming several limitations of traditional neural networks in medical applications. With these capabilities， medical foundation models are reshaping the implementation of core tasks such as assisted diagnosis， clinical report generation， and medical image analysis. They hold profound implications for achieving general intelligence in healthcare. Based on this， this paper provides a comprehensive review of the current state and future trends of medical foundation models. First， it reviews the development of medical AI models in the context of rapid advancements in artificial intelligence. Then， it highlights research progress of large models in medical subfields such as pathology， ophthalmology， and neurological disorders. Finally， it discusses the challenges currently faced by medical foundation models and explores their future development directions.