Challenges of Implementing Artificial Intelligence in Interventional Radiology

Abstract

Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and "learning" patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.

Keywords: artificial intelligence; challenges; interventional radiology; machine learning; use cases.