Computational pathology systems based on whole-slide images are often bottlenecked by the need for costly fine-grained annotations. This review surveys how multiple instance learning reduces that dependence by learning from coarse supervision while aggregating information from large-scale WSIs. It synthesizes recent advances, organizes the rapidly growing literature, and highlights the technical trends shaping modern MIL research in pathology.