Use of Artificial Intelligence in Nononcologic Interventional Radiology: Current State and Future Directions

Abstract

The future of radiology is disproportionately linked to the applications of artificial intelligence (AI). Recent exponential advancements in AI are already beginning to augment the clinical practice of radiology. Driven by a paucity of review articles in the area, this article aims to discuss applications of AI in nononcologic IR across procedural planning, execution, and follow-up along with a discussion on the future directions of the field. Applications in vascular imaging, radiomics, touchless software interactions, robotics, natural language processing, postprocedural outcome prediction, device navigation, and image acquisition are included. Familiarity with AI study analysis will help open the current “black box” of AI research and help bridge the gap between the research laboratory and clinical practice.

Artificial intelligence in interventional radiology: state of the art

“…A critical aspect for such systems is their ability to consistently track surgical and IR tools and relevant anatomical structures throughout interventional procedures, accounting for organ movement and breathing. The application of deep artificial neural networks to robotic systems helps in handling multimodal data generated in robotic sensing applications [ 36 ].…”

“…- Auloge et al demonstrated the efficacy of AI-guided percutaneous vertebroplasty, showing comparable accuracy to standard fluoroscopy with reduced fluoroscopy time [ 34 , 35 ] Robotics - AI helped in handling multimodal data generated in robotic sensing applications. [ 36 – 38 ] Touchless software interaction - Schwarz et al used AI to improve recognition rates of body gestures. [ 39 – 42 ] Virtual biopsy - Barros et al developed an AI model for digital mammography, achieving high accuracy in classifying ductal carcinoma in situ , invasive carcinomas, and benign lesions.…”

Ethical Considerations for Artificial Intelligence in Interventional Radiology: Balancing Innovation and Patient Care

“…27 As such, models built upon such data will tend to provide recommendations in line with those biases. 5 For example, if socioeconomically disadvantaged patients tend to do worse when receiving treatment compared with the overall population, AI algorithms may recommend against treating them. 9 Integrating AI systems in clinical workflows also requires the initial capital to invest in such a system and local expertise to use it.…”

“…From datadriven treatment recommendations, real-time intraprocedural support, predicting outcomes, and more, there are vast possibilities for implementing AI in interventional radiology (IR) to help maximize patient care. [2][3][4][5] While there exists much enthusiasm for integrating this cutting-edge technology in IR, there are many ethical issues to consider in its use, such as questions about data ownership and distribution, culpability in the setting of AI-associated adverse events, and amplification of inequities and bias. This article explores some of these challenges and suggests a framework for navigating them.…”

Radiomics in colorectal cancer

“…Additionally, the application of artificial intelligence is facilitating automated feature extraction and predictive modeling [35,36]. However, the development of radiomics faces several challenges [37,38]. Standardization of image acquisition protocols, feature extraction methods, and analysis workflows is crucial to ensure the reproducibility and comparability of radiomic studies.…”