mlsanityData to 14 Oct 2026

Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphs

arXiv 2309.16593 ↗First posted 28 Sep 2023cs.AI cs.LGcited by 1

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Abstract (arXiv)

Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in drug discovery and pharmaceutical research as they provide a structured way to integrate diverse information sources, enhancing AI system interpretability. This interpretability is crucial in healthcare, where trust and transparency matter, and eXplainable AI (XAI) supports decision making for healthcare professionals. This overview summarizes recent literature on the impact of KGs in healthcare and their role in developing explainable AI models. We cover KG workflow, including construction, relationship extraction, reasoning, and their applications in areas like Drug-Drug Interactions (DDI), Drug Target Interactions (DTI), Drug Development (DD), Adverse Drug Reactions (ADR), and bioinformatics. We emphasize the importance of making KGs more interpretable through knowledge-infused learning in healthcare. Finally, we highlight research challenges and provide insights for future directions.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 42 of its 128 references that are in the map. First posted on arXiv 28 Sep 2023.

Builds on

42 of 128 references.

References · 42

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30 more
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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insightscited by 7