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Related Experiment Video

Updated: Aug 1, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Knowledge graph assisted end-to-end medical dialog generation.

Deeksha Varshney1, Aizan Zafar1, Niranshu Kumar Behera1

  • 1Department of Computer Science and Engineering, IIT Patna, India.

Artificial Intelligence in Medicine
|April 26, 2023
PubMed
Summary

This study introduces a knowledge-grounded model for medical dialog systems, enhancing patient care and reducing costs. It uses medical knowledge graphs to generate clinically accurate and engaging conversations, outperforming existing methods.

Keywords:
Knowledge graphMedical dialog generationPretrained language models

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Medical Informatics

Background:

  • Medical dialog systems can improve healthcare access, quality, and cost-efficiency.
  • Current systems often generate generic responses, leading to unengaging interactions.
  • Integrating large-scale medical knowledge is crucial for advanced dialog capabilities.

Purpose of the Study:

  • To develop a knowledge-grounded conversation generation model for medical dialog systems.
  • To leverage medical knowledge graphs for improved language comprehension and generation.
  • To create clinically accurate and human-like medical conversations.

Main Methods:

  • Combined pre-trained language models with the Unified Medical Language System (UMLS) medical knowledge base.
  • Utilized a medical knowledge graph containing disease, symptom, and laboratory test information.
  • Employed MedFact attention for reasoning over knowledge graph triples and a policy network to inject relevant entities.

Main Results:

  • The model generated clinically correct and human-like medical conversations on the MedDialog-EN dataset.
  • Demonstrated significant performance improvements over state-of-the-art methods in automatic and human evaluations.
  • Showcased the effectiveness of transfer learning using a Covid-19 related dialog corpus.

Conclusions:

  • Knowledge-grounded models significantly enhance medical dialog systems.
  • Integrating medical knowledge graphs improves response generation accuracy and engagement.
  • The proposed approach offers a promising direction for advancing e-medicine through AI.