mlsanityData to 7 Oct 2026

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

arXiv 2306.04802 ↗First posted 7 Jun 2023cs.AI cs.CL cs.LGcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 75 of its 220 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

75 of 220 references.

References · 75

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 6; 2026: 4.

13 papers.

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  1. 2024SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Taskcited by 19
  2. 2025HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcarecited by 9
  3. 2023Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertaintycited by 8
  4. 2023BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insightscited by 7
  5. 2025MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graphcited by 5
  6. 2025Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMscited by 2
  7. 2025MEDAKA: Construction of Biomedical Knowledge Graphs Using Large Language Modelscited by 1
  8. 2025Data Overdose? Time for a Quadruple Shot: Knowledge Graph Construction using Enhanced Triple Extractioncited by 0
  9. 2025InvertiTune: High-Quality Data Synthesis for Cost-Effective Single-Shot Text-to-Knowledge Graph Generationcited by 0
  10. 2026Proactive Knowledge Inquiry in Doctor-Patient Dialogue: Stateful Extraction, Belief Updating, and Path-Aware Action Planningcited by 0
  11. 2026KG-CMI: Knowledge graph enhanced cross-Mamba interaction for medical visual question answeringcited by 0
  12. 2026VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphscited by 0
1 more
  1. 2026Responsible Integration of AI in Cancer Genomics: Barriers, Risks, and Pathways to Trustworthy Clinical Translationcited by 0