Construction and Applications of Knowledge Graphs in Ophthalmology

Knowledge Graph (KG) is an artificial intelligence technique that provides a structured representation of medical entities and their relationships, thereby facilitating integration of heterogeneous information, knowledge discovery, and intelligent reasoning. The process of KG construction includes knowledge acquisition, knowledge extraction, knowledge fusion, knowledge inference, knowledge graph visualization and knowledge graph evaluation. In ophthalmology, KGs have demonstrated significant potential in advancing disease understanding, supporting clinical decision-making, assisting in ophthalmic image analysis, and facilitating clinical intelligent question answering. This paper reviews the methodologies for constructing medical KGs and highlights their applications in ophthalmology, with particular emphasis on the integration of ophthalmic KGs with medical imaging and large language models (LLMs). Furthermore, it discusses existing challenges-ranging from privacy and regulatory constraints to high construction and maintenance costs, limited fusion of imaging and multimodal data, insufficient coverage of rare eye diseases and insufficient application in education and basic research-aiming to provide insights that promote deeper research and clinical translation of ophthalmology KGs.