Artificial Intelligence and Venous Thromboembolism: A Narrative Review of Applications, Benefits, and Limitations

Background: 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. Summary: 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. Key Messages: 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.