Abstract: Traditional medical image analysis methods have difficulties in quickly processing irregular or tiny targets in complex environments, while deep learning methods can capture complex implicit relationships in multimodal data and have become one of the important technologies in the field of medical image processing today. As an excellent deep learning model, YOLO has demonstrated strong capabilities in the application of medical image segmentation with its speed and accuracy. Based on recent literature research, this paper focuses on elaborating the technical characteristics, application methods and research progress of the YOLO model in medical image segmentation,with the aim of providing references for further research and clinical application in this field.