Pathology diagnosis is the "gold standard" for clinical disease diagnosis and treatment. Intelligent pathology image analysis has significant clinical value in improving diagnostic efficiency and ensuring consistency. This paper systematically reviews the core multiple instance learning paradigm for whole slide image analysis, and elaborates the pretraining system, core functional branches and latest advances of pathology foundation models. Taking clinical usability as the core evaluation dimension, it establishes a multi-dimensional evaluation framework, analyzes the clinical translation potential of different technical routes, and points out the core contradiction of "excellent laboratory performance but insufficient clinical usability" in current pathology foundation models. It further dissects the key clinical translation bottlenecks of pathology foundation models, outlines future application and development directions, and provides a reference for technological research and development as well as clinical translation in computational pathology.