Digital pathology is developing based on the improvement and popularization of WSI (whole slide imaging) scanners. WSI scanners are widely expected to be used as the next generation microscope for diagnosis; however, their usage is currently mostly limited to education and archiving. Indeed, there are still many hindrances in using WSI scanners for diagnosis (not research purpose), two of the main reasons being the perceived high cost and small gain in productivity obtained by switching from the microscope to a WSI system and the lack of WSI standardization. We believe that a key factor for advancing digital pathology is the creation of computer assisted diagnosis systems (CAD). Such systems require high-resolution digitization of slides and provide a clear added value to the often costly conversion to WSI. We (NEC Corporation) are creating a CAD system, named e-Pathologist®. This system is currently used at independent pathology labs for quality control (QC/QA), double-checking pathologists diagnosis and preventing missed cancers. At the end of 2012, about 80,000 slides, 200,000 tissues of gastric and colorectal samples will have been analyzed by e-Pathologist®. Through the development of e-Pathologist®, it has become clear that a computer program should be inspired by the pathologist diagnosis process, yet it should not be a mere copy or simulation of it. Indeed pathologists often approach the diagnosis of slides in a "holistic" manner, examining them at various magnifications, panning and zooming in a seemingly haphazard way that they often have a hard time to precisely describe. Hence there has been no clear recipe emerging from numerous interviews with pathologists on how to exactly computer code a diagnosis expert system. Instead, we focused on extracting a small set of histopathological features that were consistently indicated as important by the pathologists and then let the computer figure out how to interpret in a quantitative way the presence or absence of these features over the entire slide. Using the overall pathologists diagnosis (into a class of disease), we train the computer system using advanced machine learning techniques to predict the disease based on the extracted features. By considering the diagnosis of several expert pathologists during the training phase, we insure that the machine is learning a "gold standard" that will be applied consistently and objectively for all subsequent diagnosis, making them more predictable and reliable. Considering the future of digital pathology, it is essential for a CAD system to produce effective and accurate clinical data. To this effect, there remain many hurdles, including standardization as well as more research into seeking clinical evidences from "computer-friendly" objective measurements of histological images. Currently the most commonly used staining method is H&E (Hematoxylin and Eosin), but it is extremely difficult to standardize the H&E staining process. Current pathology criteria, category, definitions, and thresholds are all on based pathologists subjective observations. Digital pathology is an emerging field and researchers should bear responsibility not only for developing new algorithms, but also for understanding the meaning of measured quantitative data.