Integrating Artificial Intelligence into Venous Thromboembolism Care: Predictive Models, Implementation Challenges, and Future Directions.

Artificial intelligence (AI) has the potential to support personalized, multidisciplinary, data-driven care for venous thromboembolism (VTE) prevention, detection, imaging, management, and follow-up. We reviewed 23 studies: 20 prediction or detection studies were assessed with PROBAST+AI, while 3 evaluated clinical-impact interventions. Of these, 18 of 20 studies (90.0%) were at high risk of bias. Only 7 studies (35%) showed meaningful external or prospective validation, though 5/7 remained high risk of bias. Calibration was not reported in 14 studies (70.0%). Discrimination scores ranged from modest to high, but performance varied across cohorts and was often limited by retrospective design, internal validation, class imbalance, low event counts, and overfitting. Currently, agentic AI remains conceptual and needs human oversight. Future research should emphasize prospective multicenter evaluation, calibration, transparent reporting, regulatory adherence, and standardized bias auditing before AI is used routinely in clinical practice.