## 5 Discussion
### Comparison with Traditional Methods and Impact on Pathologists  
The adoption of artificial intelligence in pathology departments worldwide and the routine creation of large repositories of whole slide images in different fields of human pathology will represent a revolution in surgical pathology, with a complete transformation of daily pathology practice. Digital pathology technologies, including routine digitalization of tissue glass slides, i.e., classical histological specimens, are significantly improving all aspects of modern clinical practices, ranging from storing to reviewing, processing, diagnosing, and sharing digital scans or better whole slide images. In short, machine learning approaches are forcing the automation of significant parts of pathology workflows, thus improving the clinical abilities of histopathologists, cytopathologists, biologists, and technicians involved in clinical practice and research. The WSI-based approach is characterized by the following goals: (i) high speed in searching databases of digitized pathology reports, allowing the identification of WSIs with morphological features similar to those of the case of interest; (ii) accuracy in the interpretation of histological and immunohistochemical images, finding cases with similar morphology and learning how their mentors would diagnose the case of interest [71]; (iii) strong performance in reaching a \"good\" diagnosis in different fields of human pathology, identifying tumors that share specific features useful for clinical correlations and predicting outcome, including rare diseases; and (iv) fusing histopathology and genomic features, allowing for the identification of genomic changes associated with peculiar morphologies, useful for cancer diagnosis, therapy and prognosis [72]. In short, deep learning methods applied to the analysis of WSIs will allow pathologists to perform excellently in all fields of human pathology, with a substantial reduction in their daily workload. One of the most important applications of this new vision for pathologists working without a microscope at hand is the training of young pathologists. Pathologists in training might utilize the large repositories of WSIs to computationally identify images with similar morphological and immunohistochemical features and easily learn the fundamentals from the interpretation of biopsies, thus ending with the correct interpretation of biopsies and a correct and fast diagnosis of the case of interest. This new approach should require marked modifications in supervised training. The speed of algorithms when searching WSIs with similar morphologies compared to the case of interest will indicate the field to which the differential diagnosis should be restricted, facilitating the diagnostic process by the pathologist in training [73]. Artificial intelligence-based pathology has been shown to help pathologists in multiple fields of complexity in daily clinical practice, such as the identification of origins for cancers of unknown primary [74]. The supervision of expert pathologists might be restricted to the diagnosis of complex cases or to rare cases of human pathology not included in the algorithm yet. Scalability is a critical challenge for the adoption of the WSI-based approach in the daily practice of pathology departments worldwide. WSIs are characterized by a computationally prohibitive size, and WSI retrieval systems need a growing number of slides, each consisting of billions of pixels and measuring several gigabytes in size. This represents a challenge for the adoption of machine learning methods applied to histopathological image analysis [75].