Toward an Implementable Hybrid Digital Twin for Heart Failure: A Conceptual and Evidence-Informed Framework

Medical digital twins are increasingly proposed as patient-specific, dynamically updated computational models capable of supporting prediction and decision-making in clinical care. However, a gap persists between conceptual definitions of digital twins and their practical implementation, particularly in high-impact domains such as cardiovascular disease and heart failure. Many existing studies lack explicit synchronization mechanisms, standardized validation practices, and uncertainty-aware outputs, raising questions about their reliability and clinical applicability. In this commentary, we synthesize implementation-relevant insights from the cardiovascular digital twin literature and argue for a more explicit operationalization of “twinning” as a state-space inference and synchronization problem. We highlight the complementary strengths and limitations of mechanistic and data-driven modeling approaches, and propose that hybrid architectures—combining physiological models with machine learning–based residual components—represent a promising direction for bridging this gap. Central to this framework is the incorporation of online updating mechanisms, such as sequential Bayesian filtering, to enable continuous alignment between model predictions and patient data. We further emphasize the importance of uncertainty quantification, calibration, and reproducible evaluation as core requirements for clinically credible digital twin systems. Rather than presenting new experimental results, this work provides a conceptual and evidence-informed framework intended to guide future implementation and validation efforts. We suggest that advancing cardiovascular digital twins will require standardized benchmarks, robust validation under real-world data conditions, and integration with clinical decision-making workflows.