Artificial intelligence in clinical thrombosis and hemostasis: A review

Abstract

Background

Artificial Intelligence (AI) and machine learning (ML) are transforming hemostasis and thrombosis care, with applications spanning disease detection, risk assessment, laboratory testing, patient education, personalized medicine, and drug development. This narrative review explores AI’s clinical utility and limitations across these 6 domains.

Methods

A comprehensive search of PubMed, Embase, and Scopus (up to February 2025) was conducted using terms related to AI, thrombosis, and hemostasis. Peer-reviewed, English-language studies were included, supplemented by manual and reference screening. Of 84 studies included, 38 focused on risk assessment, 16 on diagnostics, and others on personalized medicine, drug development, and patient engagement.

Results

AI demonstrated high accuracy in diagnosing thrombotic events via imaging and electronic health record analysis, although sensitivity gaps persisted for complex cases. In laboratory settings, AI outperformed manual review in detecting errors (eg, sample mislabeling and clotted specimens). Risk stratification models surpassed traditional scores (eg, CHA₂DS₂-VASc) in predicting thromboembolism, yet inconsistently performed in cancer-associated thrombosis. Personalized anticoagulation dosing and genetic severity prediction in hemophilia highlighted AI’s precision. Chatbots and adherence tools have enhanced patient education while AI-driven drug discovery identified novel anticoagulants and repurposed existing therapies. Limitations included variable external validation, “black box” interpretability issues, and dataset biases.

Conclusion

AI offers significant promise for improving diagnostics, risk prediction, and individualized therapy in thrombosis and haemostasis. Future integration depends on transparent, validated, and equitable AI systems embedded within clinical workflows.