Semantic Reasoning in Medicine: The Role of Knowledge Graphs Across Five Key Domains

2026-06-13

AI Summary

The integration of complex, heterogeneous data and the challenges of semantic reasoning in medicine have significantly hindered advances in clinical decision-making and precision healthcare. This study presents the first systematic review of medical knowledge graph research from dual perspectives—applications (including clinical decision support, disease prediction, health recommendation, precision medicine, and medical question answering) and methodologies (encompassing ontologies, semantic web technologies, deep learning–based information extraction, and neuro-symbolic hybrid modeling). It elucidates the pivotal role of knowledge graphs in enhancing interpretability, semantic consistency, and personalized reasoning. The work traces technological evolution and real-world impact, while identifying critical challenges such as insufficient knowledge coverage, data alignment difficulties, reasoning fragility, and privacy-ethics concerns, thereby charting a path toward safe and effective medical AI systems.

Abstract

Knowledge graphs (KGs) have emerged as a promising solution for integrating and reasoning over complex biomedical and clinical data in healthcare. By representing structured relationships among entities such as diseases, drugs, symptoms, and patient records, KGs provide a semantic backbone for decision-making, prediction, recommendation, and personalized care. Recent advances have demonstrated their utility across diverse medical applications--including clinical decision support systems, disease and treatment outcome prediction, health recommender systems, precision medicine, and medical question answering--where KGs often enhance interpretability, semantic coherence, and patient-specific reasoning. In parallel, a growing body of work focuses on medical KG generation itself, proposing frameworks that construct graphs from EHRs, clinical narratives, biomedical literature, and web resources using ontologies, semantic web technologies, deep-learning-based information extraction, and hybrid neuro-symbolic pipelines. Despite this progress, significant challenges remain, including limited and fragmented knowledge coverage, difficulties in aligning heterogeneous data sources, the fragility of current reasoning and representation-learning methods on dense multi-relational graphs, and unresolved issues related to privacy, bias, and accountability. This survey reviews and categorizes current research on KGs in medicine along both application-oriented and methodology-oriented dimensions, discusses their benefits and technical foundations, and outlines key limitations and open research directions. By analyzing trends, architectures, and evaluation practices, this work aims to guide future developments in KG-driven medical AI systems and support their safe and effective integration into healthcare environments.

Problem

• knowledge graphs
• semantic reasoning
• medical AI
• heterogeneous data alignment
• representation learning

Innovation

• knowledge graphs
• semantic reasoning
• neuro-symbolic methods
• medical AI
• heterogeneous data integration