We Have the Tools But No Evidence: The Need for AI-Enhanced Intravascular Ultrasound Education Research

Background: Artificial intelligence (AI) has reached expert-level accuracy in interpreting intravascular ultrasound (IVUS) images. Automated lesion assessment shows a 0.998 correlation with expert consensus on stent sizing. However, 40% of interventional cardiology fellows struggle to identify calcium that needs modification, and 35% inaccurately size stents by more than 0.5mm—a mistake directly tied to patient complications. Despite the combination of proven technology and documented educational shortcomings, no studies have explored whether AI could help close this competency gap. Methods: Our study reviewed the existing research on AI validation in IVUS interpretation and its impact on educational outcomes in interventional cardiology training, looking for any links between these areas.Results: Our analysis shows that there are no studies that evaluate AI-enhanced IVUS education. Despite AI systems being clinically validated in 234 patients with 8,076 images, and having established multi-vendor compatibility, their use in education has not been tested. Evidence from optical coherence tomography is also concerning. While AI assistance improved efficiency by 23%, it also created dependency, with performance dropping when it was removed. Furthermore, 70-90% of users wrongly accepted incorrect AI recommendations.Conclusions: There's a significant gap in research on how to apply advanced technology in education. We suggest a series of studies to find out if using AI in education can speed up skill development, reduce bias from automation, and ultimately lead to better patient outcomes. Without this evidence, it's unclear whether implementing AI in education is beneficial or not.