Mudrik Aya, Efros Orly
Ben-Gurion University of the Negev, Be'er Sheva, Israel.
National Hemophilia Center and Institute of Thrombosis and Hemostasis, Chaim Sheba Medical Center, Tel Hashomer, Israel,
Acta Haematol. 2025 Apr 8:1-10. doi: 10.1159/000545760.
Venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, remains a leading cause of cardiovascular morbidity and mortality. Artificial intelligence (AI) holds promise for potential improvement of risk stratification, diagnosis, and management of VTE.
This narrative review explores the applications, benefits, and limitations of AI in VTE management. AI models were shown to outperform conventional methods in identifying high-risk candidates for VTE prophylaxis treatments in several postsurgical settings. It has also been demonstrated to be efficient in the early detection of VTE events, particularly through point-of-care AI-guided sonography and computer tomography image processing. Data biases, model transparency, and the need for regulatory frameworks remain significant limitations in the full integration of AI into clinical practice.
AI has the potential to improve VTE care by enhancing risk stratification and diagnosis. The integration of AI-driven models into clinical workflows has the potential to reduce costs, streamline diagnostic processes, and ensure effective management of VTE. Safe and effective integration of AI into VTE care requires addressing its limitations, such as interpretability, privacy, and algorithmic bias.
静脉血栓栓塞症（VTE），包括深静脉血栓形成和肺栓塞，仍然是心血管疾病发病和死亡的主要原因。人工智能（AI）有望改善VTE的风险分层、诊断和管理。
这篇叙述性综述探讨了AI在VTE管理中的应用、益处和局限性。在一些术后环境中，AI模型在识别VTE预防治疗的高风险候选者方面表现优于传统方法。它还被证明在早期检测VTE事件方面是有效的，特别是通过即时AI引导的超声检查和计算机断层扫描图像处理。数据偏差、模型透明度以及监管框架的需求仍然是将AI完全整合到临床实践中的重大限制。
AI有潜力通过加强风险分层和诊断来改善VTE护理。将AI驱动的模型整合到临床工作流程中有可能降低成本、简化诊断过程并确保对VTE进行有效管理。将AI安全有效地整合到VTE护理中需要解决其局限性，如可解释性、隐私和算法偏差。
