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Use of Artificial Intelligence in Non-Oncologic Interventional Radiology: Current State and Future Directions.

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Artificial intelligence (AI) is transforming non-oncologic interventional radiology (IR). This review explores AI applications in procedural planning, execution, and follow-up, aiming to bridge the gap between AI research and clinical practice.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Interventional Radiology

Background:

  • Artificial intelligence (AI) is increasingly integral to the future of radiology.
  • Significant advancements in AI are augmenting current radiological practices.
  • A lack of comprehensive review articles necessitates a focused discussion on AI in non-oncologic interventional radiology (IR).

Purpose of the Study:

  • To review and discuss the diverse applications of AI in non-oncologic IR.
  • To cover AI's role across the entire interventional radiology workflow: planning, execution, and follow-up.
  • To explore future directions and potential advancements of AI in the field.

Main Methods:

  • Review of current literature and emerging AI technologies in non-oncologic IR.
  • Categorization of AI applications by procedural phase (planning, execution, follow-up).
  • Inclusion of specific AI-driven techniques such as radiomics, natural language processing, and robotics.

Main Results:

  • AI applications span procedural planning, device navigation, and image acquisition.
  • AI enhances touchless software interactions, robotics, and vascular imaging analysis.
  • AI models show promise in predicting post-procedural outcomes and improving efficiency.

Conclusions:

  • AI integration is crucial for advancing non-oncologic interventional radiology.
  • Understanding AI methodologies is key to demystifying AI research and facilitating clinical adoption.
  • Bridging the gap between AI research and clinical practice is essential for realizing AI's full potential in radiology.