Artificial intelligence (AI) is rapidly reshaping how clinicians approach vascular disease, offering sophisticated tools that enhance diagnostic accuracy, enable earlier disease detection, and improve risk stratification across the spectrum of peripheral artery disease (PAD), pulmonary embolism (PE), carotid artery disease, and aortic disease.^1,2 As the population ages, the prevalence of vascular diseases is expected to rise. Moreover, prevalence is expected to increase due to more sophisticated imaging or AI-driven programs. Despite these developments, a wide range of vascular diseases remain underdiagnosed due to limitations in access to care and the complexity of vascular pathologies, which often worsen patient outcomes.³ The integration of AI into vascular medicine can help clinicians in early detection and diagnosis that could significantly improve patient outcomes, therefore offering a promising solution to address these long-standing challenges.

The role of AI in cardiovascular disease has been extensively discussed in literature; however, despite its significance, the impact of AI in vascular medicine remains largely overlooked. The integration of AI into vascular medicine spans three primary domains: automated image interpretation, predictive analytics, and clinical decision support. Machine learning algorithms have shown success in analyzing imaging data, such as ultrasonography and computed tomography (CT) scans, for conditions such as carotid artery disease, PE, and abdominal aortic aneurysms, which represent a significant portion of AI applications in vascular disease.^1,2-4 Moreover, AI-powered vascular imaging algorithms can analyze high-resolution modalities such as computed tomography angiography (CTA) and magnetic resonance angiography to identify subtle vascular abnormalities. These AI tools, including neural networks and support vector machines, can perform automated lesion detection, vessel segmentation, and plaque characterization with greater accuracy than traditional methods.^1-6 For instance, AI algorithms demonstrate high sensitivity (84-95%) and specificity (95-100%) for PE detection, with particular value on identifying incidental PE on nondedicated chest CT scans.⁷ In carotid artery stenosis, AI applications center on identifying vulnerable plaque characteristics that predict stroke risk beyond stenosis severity alone. Deep learning algorithms achieve pooled sensitivity of 91% and specificity of 84% for distinguishing unstable from stable plaques on imaging.⁸

Another highly significant application of AI that could greatly enhance the early detection and diagnosis of underdiagnosed vascular diseases is natural language processing (NLP).^5,6 NLP plays a crucial role by extracting structured data from unstructured clinical notes, enabling the identification of patients with early vascular disease who may otherwise go undiagnosed. Moreover, AI-driven screening programs, such as automated interpretation of ankle-brachial indexes, Doppler ultrasonography, and CTA, provide substantial improvements in early diagnosis and risk stratification.^5,6 This ultimately enables more personalized and timely interventions for patients with vascular disease through identifying those at high risk for disease progression and tailoring interventions on the basis of individual risk profiles.^5,6

For conditions such as PAD, AI has shown importance in both diagnosis and risk stratification.¹ Machine learning algorithms can analyze imaging data alongside patient medical histories to identify early signs of PAD, even in those with mild or atypical symptoms.¹ Moreover, predictive modeling can identify individuals at the highest risk for disease progression or amputation, helping clinicians create more personalized treatment plans. AI algorithms also support better classification of PAD severity and more accurate prognostication, with emerging real-time prediction models offering point-of-care decision support for complex interventions, although further validation is needed for widespread clinical use.^1,2

AI in PE primarily focuses on automated detection and risk stratification on CT pulmonary angiography. AI algorithms demonstrate high sensitivity and specificity for PE detection, with value in identifying incidental PE on nondedicated chest CT scans.⁴ These tools can reduce missed diagnoses, decrease time to detection, and serve as a safety net for radiologists. In carotid artery stenosis, AI applications centers on identifying vulnerable plaque characteristics that predict stroke risk beyond stenosis severity alone.⁸ Deep learning algorithms have been found to achieve high sensitivity of 91% and specificity of 84% for distinguishing unstable from stable plaques on imaging.⁸ AI-based segmentation tools can quantify thrombus content, calcium burden, and plaque composition on CTA, with symptomatic lesions showing significantly higher thrombus-to–total volume ratios.⁸ Ultrasonography-based AI systems improve objectivity and consistency in plaque assessment, addressing operator-dependent limitations. Moreover, for aortic disease, AI enables automated segmentation, growth prediction, and risk stratification for aneurysms and dissection.^9,10 Machine learning models, particularly U-Net architectures, facilitate monitoring of aneurysm size and characterization of dissection.^9,10 AI algorithms predict postoperative complications, death after endovascular repair, and rupture risk by integrating imaging, biomechanical, and clinical data.^9,10

"AI will not replace you—those who know how to harness AI will replace those who don't." This insight from a mentor of one of the authors has never felt more relevant. In an era of unprecedented advances in medical technology and diagnostics, the opportunity and responsibility are both in the hands of clinicians: to accelerate early detection, enhance prevention, and transform vascular medicine from reactive care to proactive, precision-driven practice. Yet, despite its promise, AI is not yet fully embedded in routine clinical care. Most studies remain unvalidated across diverse populations, and much of the research comes from specialized academic centers, leaving questions about real-world applicability. Reliable AI depends on high-quality, consistent data, and clinicians must be trained not just to use AI but to interpret, critically evaluate, and integrate its insights while safeguarding clinical judgment. The potential, however, is extraordinary. AI can reduce diagnostic burdens, sharpen the precision of interventions, and tailor treatment strategies to each patient's unique risk profile. As these tools evolve, they can revolutionize vascular care, shifting it from a one-size-fits-all, reactive model to a proactive, personalized approach that optimizes outcomes for every patient. The future is here, and those who embrace AI will shape it.

References

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2. Javidan AP, Li A, Lee MH, Forbes TL, Naji F. A systematic review and bibliometric analysis of applications of artificial intelligence and machine learning in vascular surgery. Ann Vasc Surg. 2022;85:395-405. doi:10.1016/j.avsg.2022.03.019
3. Writing Committee Members, Gornik HL, Aronow HD, et al. 2024 ACC/AHA/AACVPR/APMA/ABC/SCAI/SVM/SVN/SVS/SIR/VESS guideline for the management of lower extremity peripheral artery disease: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2024;83(24):2497-2604. doi:10.1016/j.jacc.2024.02.013
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8. Feng Y, Xu L, Shao J, et al. Artificial intelligence diagnostic performance in image-based vulnerable carotid plaque detection: a systematic review and meta-analysis. BMC Med Inform Decis Mak. 2025;25(1):419. Published 2025 Nov 11. doi:10.1186/s12911-025-03227-w
9. Lareyre F, Nasr B, Cherchi M, Chaouch N, Raffort J. Artificial intelligence in aortic disease: literature review. J Cardiovasc Surg (Torino). 2025;66(6):464-474. doi:10.23736/S0021-9509.25.13444-7
10. Hahn LD, Baeumler K, Hsiao A. Artificial intelligence and machine learning in aortic disease. Curr Opin Cardiol. 2021;36(6):695-703. doi:10.1097/HCO.0000000000000903