Artificial intelligence in interventional radiology: state of the art

Abstract

Abstract Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored. Relevance statement Exploring AI’s transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care. Key points • AI adoption in IR is more complex compared to diagnostic radiology. • Current literature about AI in IR is in its early stages. • AI has the potential to revolutionise every aspect of IR. Graphical Abstract

Keywords

• Artificial intelligence
• Deep learning
• Machine learning
• Neural networks (computer)
• Radiology (interventional)