Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling

ABSTRACT Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, underscoring the need for improved strategies in diagnosis, treatment, and disease modeling. Traditional in vitro models often fall short in replicating human CV physiology, prompting efforts to advance cardiac tissue engineering and computational modeling. Among these, three‐dimensional (3D) bioprinting has emerged as a transformative tool, enabling the creation of biomimetic CV constructs that more faithfully replicate native tissue structure and function. However, challenges persist in achieving appropriate mechanical properties and long‐term performance of engineered CV constructs. Computational modeling offers powerful solutions to assist with these challenges, providing predictive insights into structural remodeling, hemodynamics, disease progression, and therapeutic response. Techniques such as computational fluid dynamics and machine learning are increasingly used to optimize design and simulate physiological conditions. The integration of computational models with 3D bioprinting has led to hybrid platforms that enhance the precision and utility of engineered tissues. This review highlights recent advances in computational modeling applied to 3D bioprinted CV constructs, focusing on the added benefits of integrating these technologies to achieve a more accurate modeling of complex CV conditions. Together, these technologies offer a promising path toward clinically translatable, patient‐specific CV platforms.