Tissue Diagnostics is the world of pathologists, and it is increasingly becoming digitalized to leverage the enormous potential of personalized medicine and of stratifying patients, enabling the administration of modern therapies. Therefore, the daily task for pathologists is changing drastically and will become increasingly demanding, in order to take advantage of the development of modern computer technologies. The role of pathologist has rapidly evolved from exclusively describing the morphology and phenomenology of a disease, to becoming a gatekeeper for novel and most effective treatment options. This is possible based on the retrieval and management of a wide range of complex information from tissue or a group of cells and associated meta-data. Intelligent and self-learning software solutions can support and guide pathologists to score clinically relevant decisions based on the accurate and robust quantification of multiple target molecules or surrogate biomarker as companion or complimentary diagnostics along with relevant spatial relationships and contextual information from digital H&E and multiplexed images. With the availability of multiplex staining techniques on a single slide, high-resolution image analysis tools and high-end computer hardware, machine and deep learning solutions now offer diagnostic rulesets and algorithms that still require clinical validation in well-designed studies. Before entering the clinical practice, the "human factor "pathologist needs to develop trust into the output coming from the 'digital black box of computational pathology', including image analysis solutions and artificial intelligence algorithms to support critical clinical decisions which otherwise would not be available. This article is protected by copyright. All rights reserved.