## 6 Conclusions  
Different from other fields of medicine, such as radiology and clinical laboratory, which have been characterized by a recent technical revolution in terms of automatization, surgical pathology has been characterized by the persistence of old techniques and approaches. Nowadays, histopathological diagnosis, the gold standard for the correct diagnosis and therapy of human diseases, is based on the manual evaluation of colored tissue sections mounted on glasses under an optical microscope. The manual evaluation of a large number of biopsies using a microscope is time-consuming and, on some occasions, error-prone, particularly when facing rare and complex pathologic entities, for which long training is often mandatory. Consequently, pathologists involved in clinical practice are at risk of possible errors in diagnosis, and patients are affected by a delayed or erroneous diagnosis, with negative consequences on their health status [76]. The recent introduction of low-cost scanners and the possibility to obtain whole slide images from each tissue section represents a real revolution in the field of surgical pathology, with significant advantages in the evaluation of histopathological images. The medical image algorithms here reported, and the new ones that will be developed in the further months, will aid pathologists in obtaining more rapid, reliable, expert, and validated histopathological and cytological diagnoses in different fields of human pathology. Thanks to the application of machine learning techniques to pathology, pathologists in all hospitals of the world will gain access to large volumes of histopathological images previously diagnosed and validated, which could be compared with the case of interest, allowing for the quick identification of the disease class the histological specimen under examination fits with. Moreover, medical imaging algorithms could disclose details and correlations that may be undetected even by an expert's naked eye. The high resolution and multiscale nature of WSIs allow for identifying the local texture and spatial context within each histological specimen, representing a very rich data source, at a much higher resolution than that obtained using a microscope with the naked eye, which can be correlated with histopathological diagnosis and patient prognosis [32]. Based on these data, we believe that there is room for the establishment of teams of engineers specialized in AI and pathologists involved in clinical practice that can drive the development of AI technologies in pathology and develop reliable and accurate algorithms that will become clinical tools for enhancing the diagnostic accuracy of human diseases. In this project, both computer science experts and pathologists may play key roles. The role of engineers is of utmost importance, thanks to their ability to offer plenty of opportunities and explore alternative new methods, like convolutional neural networks, which have shown outstanding performance on image classification tasks [77] and improved automated classification of tissue sections. On the other hand, the role of pathologists is also of utmost importance, as they are early adopters of the algorithms and participate in the validation phase of new algorithms, ultimately ending with the development and accreditation of tools for a better diagnosis and treatment of human diseases. In conclusion, while machine learning algorithms have recently shown great promise in enhancing WSIanalysis in digital pathology, in our opinion, it is crucial to address their current limitations for the effective implementation of these tools in clinical practice. Conceptualization, G.F., M.C., L.D., F.C., M.S., and M.F. ; writing--original draft preparation, G.F. and M.F. ; writing--review and editing, G.F., M.C., L.D., F.C., M.S., L.S. and M.F. All authors have read and agreed to the published version of the manuscript.