Construction and Applications of Knowledge Graphs in Ophthalmology.

Abstract

Background: 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, KG visualization, and KG 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.

Summary: 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. 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.

Key messages: The integration of KGs with multimodal technologies represents a research frontier in the transformation of ophthalmic clinical practice, enabling more structured and precise diagnostic reasoning. Future research should focus on the construction of domain-specific KGs for rare ocular diseases and spearhead the development of multimodal knowledge frameworks to fill existing knowledge gaps, thereby ultimately enhancing both patient clinical outcomes and medical education.