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

Carolina Lanza1, Salvatore Alessio Angileri1, Serena Carriero1

  • 1Department of Health Services, Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, 20122 Milan, Italy.

Journal of Personalized Medicine
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances interventional radiology (IR) by improving planning, navigation, and assessment. Technologies like radiomics, virtual reality, and AI-powered imaging tools are transforming IR procedures for better outcomes.

Keywords:
artificial intelligence (AI)interventional radiology (IR)radiomicsroboticsthree-dimensional (3D) modelingvirtual reality (VR)

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Area of Science:

  • Medical Imaging and Interventional Radiology
  • Artificial Intelligence in Medicine

Background:

  • Interventional Radiology (IR) procedures are increasingly complex.
  • The integration of advanced technologies is crucial for improving IR outcomes.
  • Artificial Intelligence (AI) presents significant potential to enhance various aspects of IR.

Purpose of the Study:

  • To provide a comprehensive overview of the current applications and future potential of AI in Interventional Radiology.
  • To analyze key AI-driven technologies impacting IR, including radiomics, virtual reality, robotics, fusion imaging, and cone-beam computed tomography (CBCT).

Main Methods:

  • A literature review of publications over the last decades.
  • Analysis of technical developments in AI-related fields relevant to IR.
  • Examination of AI's role in pre-procedural planning, intra-procedural navigation, and post-procedural assessment.

Main Results:

  • AI significantly improves pre-procedural planning, intra-procedural navigation, and post-procedural assessment in IR.
  • Radiomics enables personalized treatment strategies through image feature extraction.
  • Virtual reality offers advanced tools for training and simulation.
  • AI-powered robotic systems enhance precision and reduce radiation exposure.
  • AI-augmented fusion imaging and CBCT improve real-time guidance and outcomes.

Conclusions:

  • AI is a transformative technology in Interventional Radiology, offering substantial improvements across the entire procedural workflow.
  • The integration of AI with tools like radiomics, VR, robotics, and advanced imaging is key to advancing IR precision, safety, and efficacy.
  • Continued research and development in AI will further revolutionize IR practice and patient care.