MRIs and other medical technology play a critical role in detecting bodily abnormalities, such as tumors, and it is widely accepted that incorporating AI can magnify this assistance. Yu et al. proposes the Medical SAM-Clip Grafting Network (MSCG) which addresses the gaps in existing AI models, such as widening algorithm training data and incorporating medical knowledge to enhance and tailor responses, especially in cases where earlier detection can alter the course of a patient’s outcome. The MSCG is split into three sections: the Dual Encoder which uses two encoders to convert the brain image into a structured numerical representation, a SC-adapting Module that derives the healthy parts of the brain, and a Fusion Decoder that generates the resulting tumor segmentation. In the experiment, the MSCG was compared with similar exemplary tumor segmentation methods. In both the quantitative and visual tests, the proposed model outperformed the others in terms of consistency between images and tumor boundary specificity. Additionally, there are four components of MSCG: the Dual Encoder, Detail Flow, Semantic Flow, and the SC-grafting module. An ablation study was conducted to determine the effects of different component losses. What was found was that the performance consistently improved with the addition of each component, suggesting benefits from every part. The effectiveness of other elements (including the use of a Mamba layer and different trainable parameters) were also tested in the study. All of these aspects combined make the proposed MSCG method competitive for medical implementation.