Clinical pathology has been adopting digital technologies in the review process of the slides. This paradigm shift is being instigated by the advances in technology that allow pathologists to remotely access data banks and visualize high-resolution images with the support of cutting-edge imaging tools. Computer-assisted diagnosis is one of such improvements that is proliferating due to the recent advancements in deep learning technologies. In this clinical area, these tools are especially relevant, due to the big spatial resolution of these images. They can identify regions of interest or diagnostically relevant features on the image, improving the screening times. This article presents the use of active learning strategies to train an object detection model for the mitotic annotation use case, developed in the iPATH research project. The model was successfully integrated into a Pathology-PACS and is being actively used by pathologists for research purposes. Results regarding the evolution and development of this model are also presented in this work.