A recent study underscores the critical need to refine automated segmentations in medical image analysis, addressing a key challenge in the field. While manual segmentation remains the gold standard for accuracy, it is notably labor-intensive, limiting scalability and efficiency. Advances in foundation models have shown significant promise in enhancing segmentation accuracy across diverse anatomical regions. These developments suggest that integrating foundation models can improve the efficiency of medical imaging workflows by reducing the reliance on exhaustive manual efforts. The study highlights how weak supervision through light feedback can be leveraged to automatically refine segmentations, potentially bridging the gap between manual precision and automated scalability. This approach aligns with ongoing research emphasizing the role of foundation models in transforming medical imaging. Overall, the findings point toward a future where medical image analysis becomes both more accurate and resource-efficient.