Artificial intelligence and machine learning in hemostasis and thrombosis
Submitted: 21 December 2023
Accepted: 17 January 2024
Published: 31 January 2024
Abstract Views: 583
PDF: 59
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Artificial intelligence (AI) is rapidly becoming more important in our daily lives, and it’s beginning to be used in life sciences and in healthcare. AI and machine learning (ML) models are just starting to be applied in the field of hemostasis and thrombosis, but there are already many examples of how they can be useful in basic research/pathophysiology, laboratory diagnostics, and clinical settings. This review wants to shortly explain how AI works, what have been its uses in hemostasis and thrombosis so far and what are possible future developments. Besides the great potential advantages of a correct application of AI to the field of hemostasis and thrombosis, possible risks of inaccurate or deliberately mischievous use of it must be carefully considered. A close monitoring of AI employment in healthcare and research will have to be applied over the next years, but it is expected that the appropriate employment of this new revolutionary technology will bring great advances to the medical field, including to the hemostasis and thrombosis area. The current review, addressed to non-experts in the field, aims to go through the applications of AI in the field of hemostasis and thrombosis that have been explored so far and to examine its advantages, drawbacks and future perspectives.



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Rashidi HH, Bowkers KA, Reyes Gil M. Machine learning in the coagulation and hemostastis arena: an overview and evaluation of methods, review of literature, and future directions. J Thromb Haemost 2023;21:728-43. DOI:
Yala A, Mikhael PG, Lehman C, et al. Optimizing risk-based breast cancer screening policies with reinforcement learning. Nat Med 2022;28:136-43. DOI:
Peréz-Sanchéz L, Patiño-Trives AM, Aguirre-Zamorano MA, et al. Characterization of antiphospholipid syndrome atherothrombotic risk by unsupervised integrated transcriptomic analyses. Arterioscler Thromb Vasc Biol 2021;41:865-77. DOI:
Kempster C, Butler G, Kuznecova E, et al. Fully automated platelet differential contrast image analysis via deep learning. Sci Rep 2022;22:4614. DOI:
Bostani A, Mirzaeibonekhater H, Najafi H, et al. MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron. Physiol Meas 2023;44. DOI:
Rösler W, Altenbuchinger M, Baeßler B, et al. An overview and a roadmap for artificial intelligence in hematology and oncology. J Cancer Res Clin Oncol 2023;149:7997-8006. DOI:
Singh D, Singh B. Investigating the impact of data normalization on classification performance. Appl Soft Comput J 2020;97:105524. DOI:
Zhou Y, Yasumoto A, Huang CJ, et al. Intelligent classification of platelet aggregates by agonist type. Elife 2020;9:e52938. DOI:
Veninga A, Baaten CCFMJ, De Simone I, et al. Effects of platelet agonists and priming on the formation of platelet populations. Thromb Haemost 2022;122:726-38. DOI:
Wang P, Sheriff J, Zhang P, et al. A multiscale model for shear-mediated platelet adhesion dynamics: correlating in silico with in vitro results. Ann Biomed Eng 2023;51:1094-97. DOI:
Fang K, Dong Z, Chen X, et al. Using machine learning to identify clotted specimens in coagulation testing. Clin Chem Lab Med 2021;59:1289-97. DOI:
Yoon GJ, Heo JN, Kim M, et al. Machine learning-based diagnosis for disseminated intravascular coagulation (DIC): development, external validation, and comparison to scoring systems. PLoS One 2018;13:e0195861. DOI:
Marcucci R, Berteotti M, Gori AM, et al. Heparin induced thrombocytopenia: position paper from the Italian Society on Thrombosis and Haemostasis (SISET). Blood Transfus 2021;19:14-23.
Nilius H, Cuker A, Haug S, et al. A machine learning model for reducing misdiagnosis in heparin-induced thrombocytopenia: a prospective, multicentre, observational study. EClinical Medicine 2022;55:101745. DOI:
Bury L, Gresele P. The amazing genetic complexity of anucleated platelets. Bleeding, Thrombosis and Vascular Biology 2022;1:33. DOI:
Antunes-Ferreira M, D'Ambrosi S, Arkani M, et al. Tumor-educated platelet blood tests for non small cell lung cancer detection and management. Sci Rep 2023;13:9359. DOI:
De Girolamo G, Sarti L, Cecoli S, et al. Safety and efficacy of treatment with vitamin K antagonists in patients managed in a network of anticoagulant services or a routine general care. Bleeding, Thrombosis and Vascular Biology 2022;1:9. DOI:
Goto S, Pieper KS, Bassand JP, et al. New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on Vitamin-K antagonists: GARFIELD-AF. Eur Heart J Cardiovasc Pharmacother 2020;6:301-9. DOI:
Labovitz DL, Shafner L, Reyes Gil M, et al. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke 2017;48:1416-9. DOI:
Lee H, Kim HJ, Chang HW, et al. Development of a system to support warfarin dose decisions using deep neural networks. Sci Rep 2021;11:14745. DOI:
Mora D, Nieto JA, Mateo J, et al; RIETE Investigators. Machine learning to predict outcomes in patients with acute pulmonary embolism who prematurely discontinued anticoagulant therapy. Thromb Haemost 2022;122:570-7. DOI:
Nafee T, Gibson CM, Travis R, et al. Machine learning to predict venous thrombosis in acutely ill medical patients. Res Pract Thromb Haemost 2020;4:230-7. DOI:
Wang Q, Yuan L, Ding X, Zhou Z. Prediction and diagnosis of venous thromboembolism using artificial intelligence approaches: a systematic review and meta-analysis. Clin AppI Thromb Haemost 2021;27:1. DOI:
Jabbour S, Fouhey D, Shapard S, et al. Measuring the impact of AI in the diagnosis of hospitalized patients: a randomized clinical vignette survey study. JAMA 2023;330:2275-84. DOI:
Khera R, Simon MA, Ross JS. Automation bias and assistive AI: risk of harm from AI driven clinical decision support. JAMA 2023;220:2255-7. DOI:
Dauerman HL, Turco JV, Fuster V. Artificial intelligence, Bob Dylan, and cardiovascular scholarship. J Am Coll Cardiol 2023;82:961-3. DOI:
Menz BD, Modi ND, Sorich MJ, Hopkins AM. Health disinformation use case highlighting the urgent need for artificial intelligence vigilance. JAMA Intern Med 2024;184:9296. DOI:
Sahni NR, Carrus B. Artificial intelligence in U.S. healthcare delivery. N Engl J Med 2023;389:348-58. DOI:
Bodini M, Rivolta MW, Sassi R. Opening the black box: interpretability of machine learning algorithms in electrocardiology. Phil Trans A Math Phys Eng Sci 2021;379:20202053. DOI:
Petch J, Di S, Nelson W. Opening the black box: the promise and limitations of explainable machine learning in cardiology. Can J Cardiol 2022;38:204-13. DOI:
Meng J, Xing R. Inside the “black box”: embedding clinical knowledge in data-driven machine learning for heart disease diagnosis. Cardiovasc Digit Health J 2022;3:276-88. DOI:
Stark L. Medicine’s lessons for AI regulation. N Engl J Med 2023;389:2213-5. DOI:
Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med 2022;28:31-8. DOI:

How to Cite

Gresele, P. (2024). Artificial intelligence and machine learning in hemostasis and thrombosis. Bleeding, Thrombosis and Vascular Biology, 2(4).