Multiple-instance learning (MIL), as a weakly supervised learning method, has been widely applied in the field of medical image analysis in recent years. The paper reviews the progress of MIL applications in whole slide images, with a detailed analysis of its roles in tumor detection, subtype classification, and survival prediction. MIL holds unique advantages in weakly supervised learning , which can be optimized and extended through the introduction of new mechanisms to adapt to a broader range of application scenarios. The paper first reviews some widely used or uniquely advantageous MIL models, elaborating on their technical features and specific application contexts. Secondly, it introduces the application and technology advancements of MIL in multimodal medical image analysis. Finally, the current research progress of MIL is summarized, and its future development prospects are explored.