The utility of machine learning in the management of patients with peripheral arterial disease

Machine learning (ML) is moving decisively from concept to clinic-adjacent evaluation in vascular medicine. Peripheral arterial disease (PAD) has become a major focus for machine learning because it is clinically heterogeneous and carries a high health and economic burden, making it well suited to data driven approaches for earlier detection and personalised risk assessment. The core promise remains unchanged: algorithms that learn patterns across multimodal data, notes, vital signs, blood results, ultrasound or duplex waveforms, CT and MR angiography, intravascular imaging, prescriptions and longitudinal outcomes can help detect disease earlier, stratify risk more reliably and match treatment to patient and lesion characteristics. The crucial question for vascular surgeons is where that promise stands today. Across diagnostics, risk stratification and outcome prediction, AI/ML is best regarded as a maturing adjunct whose best-performing tools are either in retrospective or quasi-prospective evaluation, with a small but growing body of external validation. The field is now constrained less by raw model accuracy and more by generalisability, clinical integration, explainability and governance.