This paper reviews recent efforts to adapt the Segment Anything Model (SAM) for medical image segmentation, highlighting its potential and challenges. SAM, a foundation model for image segmentation, has shown strong performance in natural image segmentation but faces challenges in medical image segmentation due to differences in image characteristics. The paper discusses SAM's zero-shot performance on various medical imaging modalities, including CT, MRI, pathology, colonoscopy, endoscopy, and multi-modal images. While SAM demonstrates competitive performance in some tasks, it struggles with complex and irregularly shaped targets, low contrast, and small structures. To improve SAM's performance in medical image segmentation, researchers have explored fine-tuning, auto-prompting, and framework modifications. Fine-tuning SAM on medical datasets has shown significant improvements in segmentation accuracy. Auto-prompting methods aim to generate prompts automatically, reducing reliance on manual input. Framework modifications, such as MedSAM and SAM-Med3D, have been developed to adapt SAM for 3D medical images and improve its performance in various medical tasks. The paper also discusses the importance of building large-scale medical datasets to enhance SAM's generalization ability. Additionally, it highlights the potential of incorporating scribble and text prompts to improve segmentation accuracy and efficiency. The integration of SAM into medical image annotation workflows has shown promise in accelerating the annotation process. Overall, while SAM shows great potential for medical image segmentation, further research is needed to address its limitations and improve its performance in complex medical scenarios.This paper reviews recent efforts to adapt the Segment Anything Model (SAM) for medical image segmentation, highlighting its potential and challenges. SAM, a foundation model for image segmentation, has shown strong performance in natural image segmentation but faces challenges in medical image segmentation due to differences in image characteristics. The paper discusses SAM's zero-shot performance on various medical imaging modalities, including CT, MRI, pathology, colonoscopy, endoscopy, and multi-modal images. While SAM demonstrates competitive performance in some tasks, it struggles with complex and irregularly shaped targets, low contrast, and small structures. To improve SAM's performance in medical image segmentation, researchers have explored fine-tuning, auto-prompting, and framework modifications. Fine-tuning SAM on medical datasets has shown significant improvements in segmentation accuracy. Auto-prompting methods aim to generate prompts automatically, reducing reliance on manual input. Framework modifications, such as MedSAM and SAM-Med3D, have been developed to adapt SAM for 3D medical images and improve its performance in various medical tasks. The paper also discusses the importance of building large-scale medical datasets to enhance SAM's generalization ability. Additionally, it highlights the potential of incorporating scribble and text prompts to improve segmentation accuracy and efficiency. The integration of SAM into medical image annotation workflows has shown promise in accelerating the annotation process. Overall, while SAM shows great potential for medical image segmentation, further research is needed to address its limitations and improve its performance in complex medical scenarios.