REVIEW article
Front. Physiol.
Sec. Vascular Physiology
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1664470
Application of Artificial Intelligence in Risk Assessment and Ma nagement of Venous Thromboembolism: Scoping review
Provisionally accepted
- 1Zhejiang Chinese Medical University, Hangzhou, China
- 2Bengbu Medical University, Bengbu, China
- 3The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
- 4Department of General Practice, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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Objective: To systematically analyze the current application status of artificial intellige nce (AI) in risk assessment and management of venous thromboembolism (VTE), eval uate the predictive performance of AI models and identify key risk factors, thereby pr oviding evidence-based references for optimizing clinical VTE prevention and treatment strategies. Methods: A scoping review framework was used. We searched for literature in both Chinese (CNKI, Wanfang, CBM) and English databases (PubMed, Web of Science, E mbase, CINAHL, and The Cochrane Library) to find studies on AI applications in VT E risk assessment, covering the time from when the databases started until March 10, 2025. By creating research questions, reviewing the literature, gathering data, and su mmarizing the results, we organized various AI models, assessed how accurately they predicted outcomes, and looked at important risk factors. Results: This review included a total of 23 studies. AI models showed better accuracy in predicting VTE risk, with AUC values between 0.740 and 0.990, greatly surpassing tradit ional scoring tools. Key risk factors identified included patient-related factors, disease-relate d factors, treatment-related factors, laboratory indicators, and catheter-related factors. Conclusion: AI technology shows remarkable advantages in VTE risk assessment by integr ating multi-source data to achieve dynamic and personalized prediction. Future research sh ould aim to conduct studies across multiple centers to confirm how useful these models ar e in real-life situations and also look into combining real-time monitoring data with AI to enhance the accuracy of preventing and treating VTE, which will help lower the number of cases and improve patient results.
Keywords: artificial intelligence, AI models, Venous Thromboembolism, Scoping review, VTE (Venous Thromboembolism)
Received: 12 Jul 2025; Accepted: 23 Sep 2025.
Copyright: © 2025 Gu, Yang, Gao, Wang, Yang and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yehong Wei, yehong8720@163.com
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