A review on the current applications of artificial intelligence in chronic venous insufficiency

A review on the current applications of artificial intelligence in chronic venous insufficiency

Objective: Chronic venous insufficiency (CVI) is a global health concern that produces a significant burden for patients and health care systems. Artificial intelligence (AI) applications are revolutionizing medicine by analyzing large data to make predictions to optimize care. This study aims to su...

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Bibliographic Details
Main Authors: Michael C. Wilkinson, MD, Andrew Ayad, MD, Sharon C. Kiang, MD, Mary L. Grewal, DO
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:JVS-Vascular Insights
Subjects:
Artificial intelligence
Chronic venous insufficiency
Machine learning
Varicose veins
Vascular surgery
Online Access:http://www.sciencedirect.com/science/article/pii/S2949912725000789
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Summary:Objective: Chronic venous insufficiency (CVI) is a global health concern that produces a significant burden for patients and health care systems. Artificial intelligence (AI) applications are revolutionizing medicine by analyzing large data to make predictions to optimize care. This study aims to summarize the current literature on ongoing applications of AI in CVI and provide awareness for future directions of AI in CVI management. Methods: A narrative review of all full-text articles evaluating AI-based interventions in CVI from inception to 2025 was performed. Results: AI applications in chronic venous disease have demonstrated improvements in understanding disease pathophysiology, predicting risk factors for the development of varicose veins, and determining which patients would benefit from interventions. Automated noninvasive thermal imaging analysis using computer vision may augment diagnosis and reduce health care delivery burden. Conclusions: Proof-of-concept applications of AI in chronic venous disease have demonstrated important utility of machine language models to accurately benefit patient care. Future focused studies are warranted to further validate machine language models to augment CVI management paradigms and streamline health care delivery.
ISSN:2949-9127

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