Abstract
Digital twins are increasingly proposed as a bridge from population-level evidence to patient-specific planning in cardiothoracic and vascular care. Early experience suggests they can be clinically consequential. In a single-center evaluation of cardiac anatomic digital twins used for heart team deliberations, management or operative strategy changed in roughly two-thirds of cases. 1
These signals are encouraging, but they also highlight the central tension in the field. The promise of precision depends on the fidelity, completeness, and governance of the data that feed the model.
Cardiac intervention is a worst-case environment for high-fidelity digital replication. The heart and great vessels are dynamic and deformable structures. Imaging is time-resolved and often depends on angle and operator technique, while intraoperative observability can be limited. Surgical data science frameworks emphasize that robust digital twins will require reliable geometric scene understanding and standardized capture of the operative field, and not only preoperative imaging. 2
At the same time, the community is exploring pragmatic bridges that include synthetic image generation, sim-to-real generalization, multimodal model adaptation, and the use of statistical “twins” as an interim step when mechanistic, fully coupled models are not yet feasible.3,4
If digital twins are to move beyond prototypes, the next bottleneck is not imagination but infrastructure. High-quality, representative datasets are the raw material for training, validating, and monitoring digital twins across institutions and patient subgroups. National or multicenter registries and interoperable electronic health records can supply longitudinal outcomes, imaging, procedural details, and adverse events at the scale needed for generalizable models.4,5
However, mega-cohorts and continuous learning systems also amplify risks, including privacy breaches, unclear rules for data access, and vulnerability to manipulation or hidden bias in model updates. These are not abstract concerns in a field where downstream recommendations can steer device selection, operative approach, or eligibility for minimally invasive therapy.
A clinically credible path forward can be stated plainly. First, build shared cardiothoracic data products with harmonized definitions (imaging protocols, procedural descriptors, endpoints) and explicit documentation of data provenance. Second, measure and report representativeness and subgroup performance, treating fairness as a model requirement rather than a post hoc analysis. Third, harden governance by using independent oversight, audit trails for model updates, and transparent disclosure of who can tune algorithms and for what purposes. Finally, evaluate digital twins prospectively, not only for accuracy but also for decision impact and patient-centered outcomes.
Digital twins may ultimately become clinical copilots for planning and surveillance but only if the field invests in trustworthy, well-governed data ecosystems that match the complexity of cardiothoracic care.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
