Purpose/Significance To provide a comprehensive review of inference methods in medical knowledge graphs that incorporate subgraph structures, and to offer valuable insights for future research in the field. Method/Process A thorough analysis of relevant literature is conducted, integrating knowledge of knowledge graphs and inference techniques. The study highlights the features and limitations of current leading models that use subgraph structures for knowledge inference, and compares them with various other inference methods. The advantages and disadvantages of these approaches are assessed in the context of specific domain tasks. Furthermore, the current state of applications and future prospects of subgraph-based inference in the medical domain are summarized. Result/Conclusion Future studies should aim to explore the interaction between diverse modalities of information in the medical field to enhance inference capabilities. This will contribute to the development of more comprehensive medical knowledge graphs, thereby providing effective solutions to clinical practical challenges.