Building digital twins for personalized cardiovascular medicine: Advances, challenges, and future directions

Authors: Claudio Chiastra, Selene Pirola, Simone Saitta, Francesco Sturla, John F. LaDisa Jr

Keywords

Artificial intelligence; Computational simulation; Digital twin; Patient-specific modeling; Personalized medicine.

Summary

This article discusses the growing role of digital twins in cardiovascular medicine, defined as dynamic virtual representations of patients that integrate clinical data with physics-based and data-driven models. Within this context, the Special Issue titled “Building digital twins for personalized cardiovascular medicine” gathers 32 studies showcasing patient-specific digital twins across multiple cardiovascular regions and devices, supporting diagnosis, risk assessment, and therapy planning. Most contributions rely on computational modeling approaches such as computational fluid dynamics, fluid-structure interaction, and electrophysiology, often combined with emerging artificial intelligence-based techniques. Despite their potential, several challenges remain, including limited clinical data, high computational costs, lack of standardization, and the need for robust validation and clinical integration. Overall, digital twins hold strong promise for advancing personalized cardiovascular care, though further technological, clinical, and regulatory progress is required for widespread adoption.

For futher details, here is the link for the open access publication.