## 6 Conclusions  
In this paper, we compared SAM 2 with SAM in medical image segmentation tasks. Regardless of our first impulses, new is not necessarily better. When deciding between SAM or SAM 2, consider precision, responsiveness, and computing resources. In particular, SAM 2 is the way to go if the best precision when segmenting medical images is required. It usually provides the same results as SAM but is better on average. In our tests, SAM 2 results get up to an average 1.76% improvement in the Jaccard index, a 1.49% improvement in the Dice Score, and a better standard deviation for both metrics, getting even closer to the state-of-the-art solutions than SAM does. However, SAM is still the best choice in a pipeline where responsiveness is critical. After processing the image to get its embeddings, SAM can predict a mask in around 61 ms. In comparison, SAM 2 takes around 588 ms to perform the same task, which is approximately ten times slower. The threshold for users to feel that the system reacts instantaneously to their actions is 100 ms [51]. SAM is below this threshold, while SAM 2 is above it. If computing resources are scarce, SAM 2 is the best choice. The best SAM 2 model is smaller than the best SAM model. At the same time, the computing time for a single prediction is faster on average. Furthermore, we presented a new medical image segmentation technique that uses a single seed prompt to segment a whole volume, further reducing the number of prompts needed to perform the task in 99.54% of the Coronacases dataset. The results are very close to the ones obtained when using one prompt for each slice, with practically no difference in the metrics used to measure the differences. Besides, we have also shown that the model is able to segment all the volumes in a dataset using a single, representative seed prompt taken from one of the volumes. In this case, the reduction in the number of prompts needed to segment the volume is 99.95% in the Coronacases dataset. This new technique could be used to virtually reduce the number of prompts needed to segment a volume up to 100%, allowing us to choose a representative seed prompt for images with similar characteristics, thus achieving infinite segmentation results with a single prompt. In future work, given the results obtained in this paper, we plan to test our segmentation technique in a case study in a hospital, integrating it into a segmentation tool. It would be interesting to study whether we can automate the creation of the artificial seed iteratively while the machine is working. Moreover, as part of this clinical implementation, future research will involve evaluating how the segmentation results influence clinical decision making and patient outcomes. This will include potential collaborations with radiology experts to gain deeper insights into the practical utility of the developed techniques, thus ensuring that the segmentation tool can be effectively integrated into real-world medical workflows. These steps are crucial in translating the technical advancements of SAM 2 into practical solutions that improve clinical practices and patient care. We will also test the technique in other medical image segmentation tasks, such as tumor segmentation, to see if the results are similar. The results obtained in this paper suggest that SAM 2 can recognize objects in images, even if they are rudimentary, and transfer that knowledge to other images. What are the characteristics of the images that allow the model to do this? How far can we refine these seed frames to make them more representative of the whole volume? Data augmentation techniques could be used to generalize the seed prompt to meet certain desirable characteristics. We have also evaluated our approach to medical imaging modalities beyond CT and X-rays, such as MRI, with promising results. Extending this evaluation to other common modalities, such as Ultrasound Sonography (USG), would further test the robustness of our method.