ReviewFrontiers in physiology2025
Application of artificial intelligence in risk assessment and management of venous thromboembolism: scoping review.
Review in Frontiers in physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Multisite Real-World Validation of an Electronic Health Record-Integrated Generative Artificial Intelligence Tool for Venous Thromboembolism Risk Stratification.medRxiv : the preprint server for health sciences · 2026Article
- Non-linear association between body mass index and port-related thrombosis in breast cancer patients: a retrospective cohort study.BMC cancer · 2026Article
- [Latent profile analysis of nursesZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning, and Natural Language Processing.Journal of cardiovascular development and disease · 2026Review
- Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness-A Review.Journal of clinical medicine · 2026Review
- AI-enabled D-dimeromics in precision breast oncology: a transformative framework for the identification, stratification, and prognostication of ultra-high-risk disease phenotypes.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To systematically analyze the current application status of artificial intelligence (AI) in risk assessment and management of venous thromboembolism (VTE), evaluate the predictive performance of AI models and identify key risk factors, thereby providing 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, Embase, CINAHL, and The Cochrane Library) to find studies on AI applications in VTE risk assessment, covering the time from when the databases started until 10 March 2025. By creating research questions, reviewing the literature, gathering data, and summarizing 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 traditional scoring tools. Key risk factors identified included patient-related factors, disease-related factors, treatment-related factors, laboratory indicators, and catheter-related factors. Conclusion: AI technology shows remarkable advantages in VTE risk assessment by integrating multi-source data to achieve dynamic and personalized prediction. Future research should aim to conduct studies across multiple centers to confirm how useful these models are 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.