For two centuries, pathology practice has woven quantitative observations with qualitative analysis. Pathologist language is spun from this web, encoded in a unique parlance that efficiently communicates both measurable features and their clinical implications. While current machine learning (ML) approaches excel at specific quantitative tasks, fully capturing the essence of the pathologist’s dynamic dialogue, in hopes of amplifying its impact, reveal a deeper challenge. Whether focusing on human-interpretable measurements or abstract deep-learning features, ML approaches often miss the sophisticated interplay between clinical observation and interpretation. Exploring the evolution of this practice, the path to computational solutions that enhance pathologist insights is revealed in preservation of their nuanced language.