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This State\u2010of\u2010the\u2010Art review examines how finite\u2010element (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\u2010specific ventricular remodeling, myocardial stress\u2013strain heterogeneity, valve\u2010ventricular coupling, and device\u2010tissue interaction are incompletely captured by conventional clinical indices. We synthesize translational and clinical literature published from 2015 to 2026 on imaging\u2010derived FE models, multimaterial 3D\u2010printed cardiovascular phantoms, and AI\u2010based analytic pipelines relevant to HF populations or HF\u2010related decision pathways. FE approaches provide mechanistic estimates of regional myocardial deformation, wall stress, and remodeling trajectories; 3D\u2010printed phantoms enable bench\u2010top 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\u2010aware, HF\u2010centered framework for integrating computational mechanics, physical phantoms, and data\u2010driven models into digital\u2010physical twin workflows. From a clinical perspective, the near\u2010term opportunity is not autonomous decision\u2010making, but better mechanistic phenotyping, more transparent procedural planning, and more rigorous testing of patient\u2010specific hypotheses before intervention. Standardized reporting, external validation, careful materials characterization, and multicenter endpoint\u2010linked studies will be essential for clinical translation.<\/jats:p>","DOI":"10.1002\/btm2.70156","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T11:53:28Z","timestamp":1781783608000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mechanical\u2010medical convergence in heart failure: Artificial intelligence, finite\u2010element modeling, and\n                    <scp>3D<\/scp>\n                    printing for diagnosis and prognosis"],"prefix":"10.1002","volume":"11","author":[{"given":"Quazi Noor E","family":"Sabrina","sequence":"first","affiliation":[{"name":"Department of Virology Dhaka Medical College and Hospital  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2398-065X","authenticated-orcid":false,"given":"Quazi Md","family":"Zobaer Shah","sequence":"additional","affiliation":[{"name":"Nuclear Safety Division Bangladesh Atomic Energy Regulatory Authority  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quazi Noor E","family":"Sohela","sequence":"additional","affiliation":[{"name":"Institute of Statistical Research and Training University of Dhaka  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Mahabub Hasan","family":"Mousum","sequence":"additional","affiliation":[{"name":"Department of Mechanical &amp; Aerospace Engineering Old Dominion University  Norfolk Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. 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