**4. Healthcare computer vision**

The advancement of computer vision in the field of medical imaging awakened in the late 2000s [4]. As partially mentioned earlier, the advances were made possible by advances in deep learning (DL) research, increased local processing capabilities with graphic processing units (GPUs), and the creation of medical image datasets [10]. The creation of larger and more complete datasets was mainly due to the increasing digitization of medical records in several countries. These electronic health records (EHR) are able to store, in addition to the images that will constitute the raw material, the labels that will be used to guide the training of the models [11]. These EHRs started out as a tool to generate billing codes for different medical practices. Then they changed their use, becoming digital support for clinical practice [12]. This change allowed its adoption not only in institutions or networks of institutions but also in entire regions and countries [12, 13]. The extension of the coverage territory allowed to expand even more the image datasets and included more patient variability, which is key to obtain models with wide generalization power.
