Abstract
The term "digital pathology" (DP) broadly refers to the process of digitizing glass whole-slide images (WSIs) using a digital whole-slide scanner as these WSIs are also used to render a diagnosis. Artificial intelligence (AI), on the other hand, is broadly defined as the application of machine-based algorithms to make a prediction, just like an intelligent human who has access to the necessary knowledge to make said prediction. Deep learning is a specific type of AI that has become very popular for the analysis and interpretation of DP images over the last decade because of the recent increase in computational power and advancements in whole-slide scanning. AI-enabled DP analysis of routine hematoxylin and eosin-stained tissues has shown increasing utility in characterizing complex tissue architecture to render disease diagnoses, prognoses, and predicting therapeutic response. A PubMed search for "digital pathology" yielded 310 hits in articles published in 2020; for those published in 2010, the same search yielded only 12 hits. The Digital Pathology Conference was initiated in 2011 by the authors (Drs. Madabhushi and Gurcan), two early pioneers in computational pathology, in anticipation of the expected explosion of research and clinical interest in this space.