Artificial intelligence and machine learning in thrombosis and hemostasis: a scoping review of clinical and laboratory applications, challenges, and future directions

Abstract

This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines to systematically map the current landscape of artificial intelligence (AI) and machine learning (ML) applications in the field of thrombosis and hemostasis (T&H), specifically targeting diagnostic enhancements in clinical and laboratory settings. Utilizing comprehensive searches across MEDLINE, EMBASE, Web of Science, and Scopus (2020-2025), 107 original studies met inclusion criteria and were analyzed. Clinical applications predominantly focused on predictive modelling for venous thromboembolism (VTE), pulmonary embolism (PE), deep vein thrombosis (DVT), anticoagulant management, and disease risk stratification, employing algorithms including neural networks, random forests, and gradient boosting. Laboratory-based AI implementations, though fewer, provided automated quality control, clot detection, and assay interpretation enhancements for potential better decision-making. Significant limitations addressed by the include studies include reliance on retrospective, single-center, small-sample datasets, limited external validation, model interpretability concerns, and integration challenges into clinical workflows. Persistent interdisciplinary disconnect between hemostasis domain experts and AI-ML specialists, compounded by regulatory hurdles, fragmented data, and labor-intensive data labelling processes, was highlighted as a major barrier to broader adoption. Recommendations for future research include developing large, externally validated multicenter datasets, transparent and interpretable ML models, prospective clinical validations, and user-centered integration strategies. Enhancing collaboration between laboratory scientists and AI-ML experts, establishing structured education programs, and creating regulatory frameworks are essential next steps to fully realize the potential of AI for significantly improving diagnostic accuracy, clinical decision-making, and patient management in T&H.

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