Knowledge Graphs Revolutionize Semantic Reasoning in Medicine

Knowledge graphs (KGs) are emerging as a powerful solution for managing and reasoning with the vast and complex data in healthcare. By structuring relationships between medical entities like diseases, drugs, symptoms, and patient records, KGs provide a robust semantic framework. This framework supports advanced applications such as clinical decision support, predicting disease outcomes, personalizing health recommendations, enabling precision medicine, and improving medical question answering systems. KGs significantly enhance the interpretability and semantic coherence of AI systems in medicine, allowing for more patient-specific reasoning. Concurrently, substantial research is dedicated to the automated generation of these medical KGs from diverse sources, including electronic health records, clinical narratives, and biomedical literature, utilizing ontologies, deep learning, and hybrid approaches. Despite considerable progress, challenges persist. These include fragmented knowledge coverage, difficulties in aligning heterogeneous data, limitations in current reasoning methods for dense multi-relational graphs, and critical issues concerning privacy, bias, and accountability. This comprehensive review categorizes current research, highlights benefits and technical foundations, and outlines future directions for integrating KGs safely and effectively into healthcare.