Artificial intelligence shows promise for improving care for peripheral artery disease through earlier detection, improved risk stratification, more tailored treatment planning, and more efficient care delivery. This American Heart Association science advisory reviews the current and emerging applications of artificial intelligence across the peripheral artery disease care continuum, including population-level screening, imaging diagnostics, outcome prediction, aneurysm risk estimation, and surgical planning. Machine learning and deep learning models demonstrate strong performance in automating peripheral artery disease detection from structured and unstructured electronic health record data, predicting major adverse cardiovascular and limb events, and enhancing diagnostic accuracy through advanced imaging analysis. Multimodal models that integrate clinical, genetic, behavioral, and biomarker data further enhance predictive precision and support individualized care strategies. Despite these advancements, real-world implementation of artificial intelligence in peripheral artery disease remains limited because of challenges in clinician training, regulatory clarity, data governance, and equitable access. We outline key barriers to adoption and propose strategies to address professional, legal, and ethical concerns, including mitigating bias and leveraging implementation frameworks. Ensuring the trustworthy, fair, and effective integration of artificial intelligence into vascular care will require interdisciplinary collaboration, ongoing validation, and robust oversight. This science advisory serves as a guide for clinicians, researchers, and policymakers on the responsible deployment of artificial intelligence in the diagnosis and management of peripheral artery disease.
Harzand et al. (Thu,) studied this question.