Mechanical-medical convergence in heart failure: Artificial intelligence, finite-element modeling, and 3D printing...

Heart failure remains a leading cause of global morbidity and mortality, yet routine clinical indices often miss the regional biomechanical disturbances that drive progression and shape treatment response. This State-of-the-Art review examines how finite-element (FE) modeling, additive manufacturing, and artificial intelligence (AI) are converging to improve the diagnosis, phenotyping, procedural planning, and prognostic assessment of heart failure (HF). Although these technologies have matured in structural heart disease and transcatheter intervention research, their greatest translational potential may lie in HF, where patient-specific ventricular remodeling, myocardial stress-strain heterogeneity, valve-ventricular coupling, and device-tissue interaction are incompletely captured by conventional clinical indices. We synthesize translational and clinical literature published from 2015 to 2026 on imaging-derived FE models, multimaterial 3D-printed cardiovascular phantoms, and AI-based analytic pipelines relevant to HF populations or HF-related decision pathways. FE approaches provide mechanistic estimates of regional myocardial deformation, wall stress, and remodeling trajectories; 3D-printed phantoms enable bench-top validation, device rehearsal, and hemodynamic replication; and AI supports segmentation, phenotyping, multimodal integration, and risk prediction. Across all three domains, however, the evidence base remains dominated by retrospective studies, bench validation, and small translational cohorts, with limited prospective outcome validation and inconsistent reporting of reproducibility. We therefore propose an evidence-aware, HF-centered framework for integrating computational mechanics, physical phantoms, and data-driven models into digital-physical twin workflows. From a clinical perspective, the near-term opportunity is not autonomous decision-making, but better mechanistic phenotyping, more transparent procedural planning, and more rigorous testing of patient-specific hypotheses before intervention. Standardized reporting, external validation, careful materials characterization, and multicenter endpoint-linked studies will be essential for clinical translation.