Current and emerging artificial intelligence applications for pediatric interventional radiology

Artificial intelligence in medicine can help improve the accuracy and efficiency of diagnostics, selection of therapies and prediction of outcomes. Machine learning describes a subset of artificial intelligence that utilizes algorithms that can learn modeling functions from datasets. More complex algorithms, or deep learning, can similarly learn modeling functions for a variety of tasks leveraging massive complex datasets. The aggregation of artificial intelligence tools has the potential to improve many facets of health care delivery, from mundane tasks such as scheduling appointments to more complex functions such as enterprise management modeling and in-suite procedural assistance. Within radiology, the roles and use cases for artificial intelligence (inclusive of machine learning and deep learning) continue to evolve. Significant resources have been devoted to diagnostic radiology tasks via national radiology societies, academic medical centers and hundreds of commercial entities. Despite the widespread interest in artificial intelligence radiology solutions, there remains a lack of applications and discussion for use cases in interventional radiology (IR). Even more relevant to this audience, specific technologies tailored to the pediatric IR space are lacking. In this review, we describe artificial intelligence technologies that have been developed within the IR suite, as well as some future work, with a focus on artificial intelligence's potential impact in pediatric interventional medicine.