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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Prime Time for Artificial Intelligence in Interventional Radiology.

Jarrel Seah1,2, Tom Boeken3, Marc Sapoval3

  • 1Department of Radiology, Alfred Health, Melbourne, VIC, Australia.

Cardiovascular and Interventional Radiology
|January 15, 2022
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Summary

Artificial intelligence (AI) is transforming diagnostic radiology and interventional radiology (IR). This review explores AI applications in IR, addressing data challenges and research best practices for future involvement.

Keywords:
AIArtificial intelligenceDeep learningInterventional radiologyMachine learning

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Area of Science:

  • Radiology
  • Interventional Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Machine learning (ML), a subset of artificial intelligence (AI), is poised to revolutionize diagnostic radiology workflows and capabilities.
  • The application of AI in Interventional Radiology (IR) is rapidly gaining momentum, positioning IR at the forefront of AI research within procedural medicine.
  • This burgeoning interest suggests IR could pioneer AI advancements applicable to all interventional medical specialties.

Purpose of the Study:

  • To provide a comprehensive overview of ML, radiomics, and AI within the field of Interventional Radiology.
  • To enumerate potential applications of these advanced computational techniques in IR.
  • To describe strategies for mitigating the challenge of limited data in AI applications specific to IR.

Main Methods:

  • Literature review and synthesis of current research on AI, ML, and radiomics in Interventional Radiology.
  • Analysis of existing and potential clinical applications of AI in IR procedures.
  • Identification and discussion of methods to address data scarcity issues in AI development for IR.

Main Results:

  • AI offers significant potential to enhance workflow and diagnostic accuracy in Interventional Radiology.
  • Radiomics and ML techniques can extract valuable prognostic and predictive information from medical images.
  • Specific strategies exist to overcome data limitations, enabling robust AI model development in IR.

Conclusions:

  • Interventional Radiology is well-positioned to lead AI research and clinical integration across medical fields.
  • Addressing data challenges and common research pitfalls is crucial for advancing AI in IR.
  • This review provides a roadmap for researchers interested in AI and its application in Interventional Radiology.