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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Knowledge graph based reasoning in medical image analysis: A scoping review.

Qinghua Huang1, Guanghui Li2

  • 1School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, 710072, Shaanxi, China.

Computers in Biology and Medicine
|September 8, 2024
PubMed
Summary
This summary is machine-generated.

Knowledge graphs enhance automated medical diagnosis systems by improving data organization and interpretability. This review explores their application in computer-aided diagnosis (CAD) and suggests future research directions.

Keywords:
Knowledge graphMedical diagnosisMedical expert systemsMedical image analysis

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Knowledge Representation

Background:

  • Computer-aided diagnosis (CAD) is increasingly vital in medicine, driven by AI and hardware advancements.
  • Knowledge graphs offer structured, interpretable representations of complex information.
  • Integrating knowledge graph inference into CAD presents significant research potential.

Purpose of the Study:

  • To review the fundamental principles and applications of knowledge graphs.
  • To systematically analyze the use of knowledge graphs in medical imaging-assisted diagnosis.
  • To identify current research limitations and propose future directions.

Main Methods:

  • Review of basic knowledge graph principles and application methodologies.
  • Systematic analysis of existing research on knowledge graphs in medical imaging CAD.
  • Identification and summary of current research shortcomings.

Main Results:

  • Knowledge graphs show promise for organizing and interpreting large-scale medical knowledge.
  • Applications in medical imaging-assisted diagnosis have been explored.
  • Key challenges include data barriers, multimodal information utilization, and interpretability.

Conclusions:

  • Knowledge graph inference holds potential for advancing CAD systems.
  • Addressing data limitations, multimodal integration, and interpretability is crucial for future development.
  • Further research is needed to fully leverage knowledge graphs in medical diagnosis.