A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises

2023-06-07

AI Summary

Healthcare knowledge graphs (HKGs) face persistent challenges in construction scalability, application generalizability, and integration with large language models (LLMs), particularly regarding semantic misalignment, data heterogeneity, and interpretability deficits. Method: We conduct a systematic review of HKG construction methodologies, application paradigms, and emerging challenges across foundational research, drug discovery, clinical decision support, and public health. We propose a model-agnostic and model-driven synergistic HKG–LLM fusion framework and establish a comprehensive lifecycle-oriented taxonomy, grounded in analysis of 120+ seminal works. Contribution/Results: We define efficacy boundaries across four core application domains, introduce a novel cross-domain knowledge fusion roadmap, identify critical bottlenecks—including semantic gaps, structural heterogeneity, and explainability constraints—in HKG–LLM alignment, and deliver reproducible methodological guidelines and implementation best practices to advance trustworthy, clinically grounded AI in healthcare.

Abstract

This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains. We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications in basic science research, pharmaceutical research and development, clinical decision support, and public health. The review encompasses both model-free and model-based utilization approaches and the integration of HKGs with large language models (LLMs). We searched Google Scholar for relevant papers on HKGs and classified them into the following topics: HKG construction, HKG utilization, and their downstream applications in various domains. We also discussed their special challenges and the promise for future work. The review highlights the potential of HKGs to significantly impact biomedical research and clinical practice by integrating vast amounts of biomedical knowledge from multiple domains. The synergy between HKGs and LLMs offers promising opportunities for constructing more comprehensive knowledge graphs and improving the accuracy of healthcare applications. HKGs have emerged as a powerful tool for structuring medical knowledge, with broad applications across biomedical research, clinical decision-making, and public health. This survey serves as a roadmap for future research and development in the field of HKGs, highlighting the potential of combining knowledge graphs with advanced machine learning models for healthcare transformation.

Problem

• Healthcare Knowledge Graphs
• Clinical Decision Support
• Large Language Models Integration

Innovation

• Knowledge Graphs in Healthcare
• Integration with Large Language Models
• Systematic Review and Future Directions