Traditional evidence-based workflows, progressing sequentially from laboratory testing to animal studies and clinical trials, have ensured safety and standardization but remain slow, costly, and only moderately predictive of clinical outcomes. This narrative review introduces a digital translational framework that reconceptualizes validation as an iterative, data-driven continuum integrating computational modeling, targeted experimentation, and real-world clinical monitoring. A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science using MeSH/EMTREE and free-text terms structured across four thematic domains: (1) digital dentistry, (2) design and manufacturing, (3) computational modelling and artificial intelligence, and (4) validation frameworks. A total of 76 studies (n = 76) were included and qualitatively synthesized through a structured evidence matrix. Recent advances in finite element analysis (FEA), artificial intelligence (AI), digital twins, and additive manufacturing demonstrate how virtual prototyping, adaptive experimentation, and continuous learning can transform biomaterials development. By shifting from a linear, empirical pipeline to an integrated, predictive paradigm, this model-based approach promises to accelerate innovation, reduce translational attrition, and enable personalized interventions. Adopting such digital validation frameworks represents a critical step toward bridging the persistent gap between laboratory innovation and clinical impact, supporting a more transparent, efficient, and patient-centered future for dentistry. • Model-based approach enables predictive and personalized dentistry • Continuous learning bridges laboratory and clinical data • Digital-first framework replaces empirical testing with data-driven validation • Adaptive validation accelerates translation and reduces development attrition • Digital methodologies strengthen the link between innovation and patient care
Tonin et al. (Thu,) studied this question.