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Area of Science:

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

Background:

  • Artificial intelligence (AI) presents significant opportunities to advance medical practice.
  • Integrating AI into procedural medicine, specifically interventional radiology (IR), faces unique challenges.
  • A clear vision for AI in IR workflows is needed to guide theoretical applications.

Purpose of the Study:

  • To outline a futuristic AI-driven interventional radiology (IR) workflow.
  • To redefine the scope of AI applications within IR procedures.
  • To enhance procedural management, patient outcomes, and safety through AI integration.

Main Methods:

  • Semiautomated diagnosis and preprocedural planning using AI-driven multidisciplinary tumor boards.
  • AI-guided outcome prediction, noninvasive imaging biopsy, personalized therapeutics, and simulations.
  • AI-enhanced ablation techniques including automatic segmentation, robotic stereotaxy, and iterative sphere packing.
  • AI virtual outcome prediction for preplanning robotic endovascular interventions.

Main Results:

  • AI can automate and optimize various aspects of IR procedures, from diagnosis to post-procedural analysis.
  • The proposed workflow integrates advanced AI tools for enhanced precision in ablations and endovascular interventions.
  • AI enables predictive modeling for interventional procedures, minimizing risks and anesthesia time.

Conclusions:

  • Responsible AI integration can significantly enhance the mission of interventional radiology.
  • AI promises to make IR more personalized, minimally invasive, cost-effective, and safer.
  • The outlined futuristic workflow provides a roadmap for advancing AI in procedural medicine.