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

Knowledge graphs improve AI transparency in healthcare by organizing complex medical information into structured, interpretable formats — this structured approach helps healthcare professionals understand how AI systems reach conclusions about drug interactions, treatments, and patient outcomes, which is essential for clinical decision-making.

Knowledge graphs enable AI systems to explain their reasoning across multiple healthcare applications, including drug-drug interactions, adverse drug reactions, and drug development, allowing doctors and researchers to trace why an AI system flagged a particular drug combination or predicted a specific outcome rather than treating it as a "black box."

The field still faces significant challenges in making knowledge graphs themselves more interpretable — the paper emphasizes that while KGs enhance AI explainability, there is ongoing research needed to make the knowledge graphs themselves easier for healthcare professionals to understand and validate, particularly through "knowledge-infused learning" approaches.

Integrating diverse medical data sources through knowledge graphs reduces errors and improves trust in AI recommendations — by combining information from multiple sources (research databases, clinical records, pharmaceutical data) into a unified knowledge graph, healthcare AI systems can provide more reliable and transparent recommendations that professionals can confidently act upon.

Future healthcare AI systems will need to balance technical sophistication with practical usability — the paper highlights that explainability through knowledge graphs is not just a technical feature but a requirement for responsible AI deployment in healthcare, where transparency directly impacts patient safety and professional accountability.