Artificial intelligence in interventional radiology: Current concepts and future trends

Abstract

• New foundation models transform the handling of multimodal data, enabling the development of patient selection strategies and outcome prediction models. • Integrating artificial intelligence into robotics paves the way for fully automated interventions. • Leveraging data augmentation can significantly advance research in interventional radiology. • There is an urgent need for comprehensive regulatory guidelines to ensure the safe and effective implementation of artificial intelligence in clinical practice. While artificial intelligence (AI) is already well established in diagnostic radiology, it is beginning to make its mark in interventional radiology. AI has the potential to dramatically change the daily practice of interventional radiology at several levels. In the preoperative setting, recent advances in deep learning models, particularly foundation models, enable effective management of multimodality and increased autonomy through their ability to function minimally without supervision. Multimodality is at the heart of patient-tailored management and in interventional radiology, this translates into the development of innovative models for patient selection and outcome prediction. In the perioperative setting, AI is manifesting itself in applications that assist radiologists in image analysis and real-time decision making, thereby improving the efficiency, accuracy, and safety of interventions. In synergy with advances in robotic technologies, AI is laying the groundwork for an increased autonomy. From a research perspective, the development of artificial health data, such as AI-based data augmentation, offers an innovative solution to this central issue and promises to stimulate research in this area. This review aims to provide the medical community with the most important current and future applications of AI in interventional radiology.

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