Editorial: Innovative modeling and simulation in thrombosis and hemostasis: enhancing diagnosis and treatment

Computational modeling has become an increasingly important component of thrombosis and hemostasis research, evolving from mechanistic simulations into multidisciplinary approaches that support diagnosis, risk stratification, and therapeutic decision-making. The fourteen contributions included in this Research Topic illustrate this evolution through complementary methodologies, including mechanistic modeling, computational fluid dynamics, machine learning, quantitative imaging, biomarker integration, real-world data analysis, and evidence synthesis. Collectively, these studies demonstrate how biological understanding, individualized prediction, comprehensive patient characterization, and rigorous validation represent interconnected steps toward clinically meaningful computational tools. Rather than competing methodologies, these approaches address complementary dimensions of thrombosis and hemostasis, contributing to more integrated frameworks for precision medicine. Together, this collection highlights both the progress achieved and the challenges that remain in translating computational innovation into robust clinical decision-support systems.