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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Towards a Knowledge Graph-Based Explainable Decision Support System in Healthcare.

Enayat Rajabi1, Kobra Etminani2

  • 1Cape Breton University, Sydney, NS, Canada.

Studies in Health Technology and Informatics
|May 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a knowledge graph-based framework to enhance the explainability of artificial intelligence (AI) in clinical decision support systems. This improves transparency for healthcare professionals.

Keywords:
Clinical decision support systemExplainable AIKnowledge Graph

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Knowledge Representation

Background:

  • Clinical decision support systems (CDSS) powered by AI offer predictive insights.
  • Lack of transparency in AI decisions hinders healthcare professional trust and adoption.
  • Explainable AI (XAI) is crucial for understanding AI-driven medical predictions.

Purpose of the Study:

  • To propose a novel framework for enhancing the explainability of AI-based CDSS.
  • To leverage knowledge graphs for transparent and understandable AI predictions in healthcare.
  • To improve the integration of eXplainable AI (XAI) in clinical settings.

Main Methods:

  • Development of a knowledge graph-based explainable framework.
  • Integration of knowledge graphs with AI algorithms in CDSS.
  • Evaluation of the framework's effectiveness in improving explanation transparency.

Main Results:

  • The proposed framework significantly increases the explainability of AI-based CDSS.
  • Knowledge graphs provide a structured approach to elucidate AI decision-making processes.
  • Enhanced transparency facilitates better understanding for healthcare professionals.

Conclusions:

  • Knowledge graph integration is a viable strategy for advancing XAI in clinical decision support.
  • The framework offers a pathway to more trustworthy and interpretable AI in healthcare.
  • Future work should focus on broader clinical validation and implementation.