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

TL;DR

This paper reviews how knowledge graphs enhance explainability in healthcare AI, focusing on their construction, applications, and future challenges to improve trust and transparency in medical decision-making.

Contribution

It provides a comprehensive overview of recent advances in applying knowledge graphs for explainable healthcare AI, highlighting workflows, applications, and interpretability techniques.

Findings

Knowledge graphs improve interpretability in healthcare AI applications.

Knowledge-infused learning enhances the interpretability of KGs.

Identifies key challenges and future research directions in healthcare KGs.

Abstract

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.