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.

翻译：知识图谱（KGs）在医疗保健人工智能领域日益受到重视，尤其在药物发现和制药研究中，因其能以结构化方式整合多源信息，显著提升AI系统的可解释性。这种可解释性在注重信任与透明度的医疗场景中至关重要，而可解释人工智能（XAI）能够辅助医疗专业人员的决策制定。本文综述了知识图谱在医疗领域的影响及其在构建可解释AI模型中的作用。我们系统梳理了知识图谱的工作流程，涵盖构建、关系抽取、推理等环节，并探讨其在药物相互作用（DDI）、药物靶点相互作用（DTI）、药物研发（DD）、药物不良反应（ADR）及生物信息学等领域的应用。重点强调通过知识注入学习提升医疗知识图谱可解释性的重要性。最后，我们指出了当前研究面临的挑战，并对未来发展方向提供了见解。