Abstract
Tumor cellularity (TC) is a key histopathological measure that impacts the quality of molecular testing and the choice of treatment. Estimation of TC is usually manual, by visual assessment by pathologists, which makes it prone to inter-observer variability. Advancements in the application of artificial intelligence (AI) in digital pathology facilitate TC evaluation and scale up existing pathology workflows. In practical terms, these AI systems analyze digitized tissue slides computationally, producing objective TC scores that can support pathologists in determining whether a specimen contains sufficient tumor material for molecular testing. This review presents a compilation of AI-driven methodologies for TC estimation by summarizing publicly available datasets and benchmarking resources, examining methodological advancements in automated TC assessment, and delineating the research and commercial tools accessible for TC quantification. It also discusses the challenges that hinder clinical translation and the necessity for transparent and interpretable outputs that align with the pathologists' reasoning.