**10. Challenges for the field**

As we briefly mentioned in one of the previous sections, one of the biggest challenges facing the field of AI and computer vision applied to medicine is the availability of datasets. Generating general datasets, although it is a task that requires time, can be done in a more laborsaving way. For instance, it does not require a high degree of training to classify common images. In fact, some search engines ask their users when they access specific content to first select from a group of images those that have a traffic light in it. That generates labels and in this way very large datasets are built. As we also mentioned before, in order to generate medical image datasets, trained doctors are needed to perform the same activity. That requirement makes the process complex, time-consuming, and expensive [25, 26]. Another problem facing the field is the variability between different hospital centers'samples. As we have already explained before, the greater the amount of data that the algorithm trains with, the higher its generalization power. However, when the data comes from different hospitals, even if they are in the same city, samples of the same medical condition may suffer variations in color, brightness, contrast, and position, to mention just a few. These variations respond to the different equipment used by hospitals and the
