How Artificial Intelligence Is Transforming Interventional Radiology

Artificial intelligence (AI) is emerging as a powerful force in medicine, and its influence on interventional radiology (IR) is growing rapidly. IR is a clinical specialty focused on performing minimally invasive procedures, guided by imaging, to diagnose and treat disease. Because these procedures rely heavily on imaging interpretation, navigation, and precise manipulation tools, AI and related technologies (like robotics and augmented imaging) have natural applications that improve accuracy and patient outcomes.

What Interventional Radiology Entails

Interventional radiologists use imaging modalities – such as ultrasound, CT, fluoroscopy, and MRI – to guide needles, catheters, and other devices to treat various conditions, including tumors, vascular diseases, and bleeding. This procedural nature generates a large volume of imaging and operational data, making the specialty a promising ground for AI tools that can assist with image interpretation, decision support, and workflow optimization.

AI in Practice: Key Applications and Tools

• AI Enhanced Pre-Procedural Plannin

Before a procedure is performed, clinicians must carefully plan the route and technique based on multiple imaging datasets.

Example Tools & Technologies:

• Image Segmentation & Fusion Software: Platforms such as Philips’ Emboguide and XperGuide (Philips Healthcare) use advanced imaging algorithms, often enhanced by AI, to map vascular pathways and target lesions before chemoembolization or cryoablation. These tools help visualize complex anatomy on CT or cone-beam CT and overlay them seamlessly for planning.
• Radiomics-Based Prediction Models: AI models extract detailed features from images to predict how patients might respond to treatments such as transarterial chemoembolization (TACE), improving patient selection and personalized plans.

Case Example: In a liver cancer embolization case, software like Emboguide was used to map arterial pathways to multiple lesions using angiographic and CT data, aiding radiologist in navigating complex vessels before treatment.

• AI-Assisted Intra-Procedural Imaging and Navigation

During the procedure itself, real-time imaging and tool guidance are critical.

AI-Integrated Systems in Use or Development:

• AI-Enhanced Fluoroscopy & Ultrasound: AI algorithms can improve image quality, reduce noise, and highlight key structures to guide needle placement, especially in ultrasound-guided interventions where visibility can be challenging.
• Fusion Imaging Platforms: Systems that overlay CT, MRI, or pre-acquired 3D models onto live fluoroscopy help clinicians navigate instruments with greater confidence.
• Robotic Guidance Platforms: While many robotics systems originated without AI, several modern platforms integrate AI logic and improved visualization to assist in precise movements.
  • Maxio (Perfint Healthcare) and iSYS (iSYS Medizintechnik GmbH) are robotic systems used for percutaneous CT-guided procedures. These robots help guide needles for biopsies and ablations with high reproducibility and may incorporate software that uses advanced image processing techniques.
  • Soft robotic microcatheters developed in research settings help navigate tortuous vasculature more easily than conventional catheters.

Case Example: Robotic-assisted percutaneous needle insertion has been demonstrated in liver tumor ablation contexts, where robotic guidance offered both accuracy and reduced radiation exposure because the needle can be inserted with minimal fluoroscopy.

• Post-Procedure Evaluation and Follow-Up

After a procedure, clinicians must confirm treatment success and identify complications early.

AI Contributions:

• Automated Volumetric Analysis: AI can quantify changes in tumor size or vessel patency more precisely than manual measurements, providing objective data for post-treatment monitoring.
• Complication Detection Algorithms: AI algorithms can flag early signs of complications (e.g., bleeding or infarction) on follow-up imaging.

Case Example: AI image processing of post-ablation CT scans can help distinguish between normal post-procedure changes and early residual disease, enabling faster clinical decisions.

Specific AI & Imaging Software Used Clinically

While many AI tools are developed for diagnostic radiology, several have relevance or emerging use within or alongside interventional radiology:

• AI Reporting & Workflow Tools
  • Rad AI: Automates report generation from imaging studies, reducing turnaround time and improving consistency.
• AI-Driven Image Enhancement & Interpretation
  • Subtle Medical: Enhances low-dose or noisy imaging to produce clearer scans, potentially reducing radiation or repeat imaging.
  • Enlitic & other AI platforms (e.g., Aidoc, Qure.ai) widely used in diagnostic settings but capable of supporting IR teams by pre-flagging critical findings or prioritizing scans.

These tools don’t “do the procedure” but enhance the informational context that interventional radiologists rely on during planning and evaluation.

Case Examples Illustrating AI Use in Interventional Contexts

AI-Guided Tumor Ablation Planning

In cases of hepatic tumors treated with microwave or radiofrequency ablation, AI-enhanced imaging helped clinicians plan ablation zones by integrating pre-procedural MRI or CT with real-time imaging to optimize antenna placement and estimate resulting necrotic zones. Software like Philips’ fusion systems and CBCT tools supports this integrated workflow.

AI-Assisted Vascular Interventions

AI-enhanced segmentation and pattern detection can aid complex endovascular cases by highlighting vessel stenosis or aneurysmal changes on CTA or fluoroscopy. While these often fall under diagnostic radiology AI, the outputs directly assist IR teams in planning and navigating interventional devices.

Challenges and Future Directions

Despite progress, several hurdles slow widespread clinical adoption:

• Dataset Limitations: Compared to diagnostic radiology, IR procedures generate highly variable and unstructured data, making AI training difficult.
• Regulatory & Safety Requirements: AI tools intended to guide interventions must meet strict clinical standards, often requiring extensive validation.
• Workflow Integration: Seamlessly integrating AI into already complex procedural environments remains a workflow challenge.

Finding the Right Specialist

With AI helping expand what interventional radiology can treat, more patients are discovering alternatives to traditional surgery. If you’re considering a minimally invasive, image-guided procedure, working with the right interventional radiologist matters.

Find an interventional radiologist on Doctorize to explore specialists near you and learn more about your options.