Venous thromboembolism (VTE), which includes deep vein thrombosis and pulmonary embolism, is a significant and preventable cause of morbidity and mortality worldwide. Despite the existence of clinical prediction models, biomarker-based risk assessments, and imaging techniques, gaps remain in accurately identifying and managing high-risk patients. In recent years, artificial intelligence has emerged as a transformative tool in healthcare, offering promising applications for enhancing VTE prevention strategies. This narrative review synthesizes current evidence on the use of artificial intelligence (AI) technologies including machine learning (ML), deep learning (DL), and natural language processing (NLP). We explore how supervised ML algorithms, such as random forests, support vector machines, and gradient boosting, improve predictive performance compared to traditional models by capturing complex, nonlinear relationships within electronic health record data. We also examine the role of DL models, particularly convolutional neural networks, in interpreting imaging data, achieving diagnostic accuracies comparable to expert radiologists. Additionally, the review highlights NLP applications in extracting risk-relevant information from unstructured clinical notes and the emerging integration of wearable device data and time-series analysis for dynamic risk assessment. We argue that the successful integration of AI into routine VTE prevention workflows requires rigorous prospective validation, cross-institutional collaboration, and thoughtful implementation into clinical decision support systems.

Background: Pulmonary embolism (PE) is a major cause of morbidity and in-hospital mortality and requires rapid clinical assessment for diagnosis, risk stratification, and management. Several validated clinical prediction tools, including the Pulmonary Embolism Severity Index (PESI), WELLS, PADUA, and IMPROVE scores, are commonly used to evaluate thromboembolic risk and predict clinical outcomes. Methods: A retrospective observational study was conducted on a cohort of 538 patients diagnosed with PE, all recruited between January 2020–December 2025. Group comparisons between survivors and non-survivors were performed using independent samples t-tests and Mann–Whitney U tests. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of clinical scores. Multivariable logistic regression analysis was performed to identify independent predictors of in-hospital mortality. Results: Mean age was 69 years, and overall death-rate was 18.4%. Significant differences between survivors and non-survivors were observed for age and clinical scores. White blood cell count, neutrophils, lymphocytes, platelet count, total bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), procalcitonin, and international normalized ratio (INR), were significantly associated with in-hospital mortality. ROC curve analysis demonstrated predictive performance of the evaluated clinical scores. Logistic regression identified PESI score, procalcitonin levels, and white blood cell count as independent predictors of unfavorable outcome. mortality. Conclusions: Clinical risk scores and specific laboratory biomarkers were associated with in-hospital mortality in patients with pulmonary embolism. The PESI score, procalcitonin, and white blood cell count showed independent predictive value for death rate in this cohort.

Background: Pulmonary embolism (PE) remains a major cause of cardiovascular morbidity and mortality, and associated infections may modify its clinical course. This study evaluated the impact of viral infections on pulmonary embolism severity, mortality, and the performance of established risk scores. Methods: This retrospective observational study included 729 patients with confirmed PE admitted between January 2020 and December 2025. Patients were grouped according to the presence of associated infections. We analysed clinical data, comorbidities, thrombus localization, laboratory parameters, infection type, treatment, mortality, and Pulmonary Embolism Severity Index (PESI), Wells, Padua, and International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) scores. We performed between-group comparisons, logistic regression, and ROC curve analyses. Results: Patients with associated infections had higher crude mortality than those without infections (27.7% vs. 18.4%, p = 0.006). They also showed higher PESI and IMPROVE scores for venous thromboembolism (VTE), increased white blood cell (WBC) count, markedly elevated D-dimers, longer prothrombin time, and different thrombus localization. Viral infection category was independently associated with mortality, with the IMPROVE score showing the highest ROC discrimination among evaluated scores. Conclusions: Associated infections define a higher-risk PE phenotype. Risk stratification should integrate infection status, laboratory markers, and conventional PE/VTE scores.