Artificial intelligence (AI) encompasses computational algorithms that, partially or completely, autonomously perform beneficial tasks usually considered representative of human intelligence.[1] This revolutionary technology has the potential to shape the scope of healthcare in incredible ways. From data-driven 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]
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[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.
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