A Digital Twin (DT) is a set of virtual information constructs that mimics the structure, context, and behavior of a natural, engineered, or system-of-systems, is dynamically updated with data from its physical twin, has a predictive capability, and informs decisions that realize value. The bidirectional interaction between the virtual and the physical systems is central to functionalizing a digital twin. Biomedical DTs are poised to revolutionize biomedical innovation by enabling predictive, real-time modeling of complex biological and engineered systems. Yet despite their potential, most biomedical DTs fail to reach deployment due to a fundamental misalignment with clinical workflows, regulatory structures, usability requirements, and systems-level constraints. This Vision and New Ideas paper introduces a new class of digital twins, Fit-for-Implementation Digital Twins (FiDTs), designed to address this translational bottleneck through an engineering-informed, stakeholder-driven, and lifecycle-integrated strategy. Aiming to accelerate the end-to-end translation of next-generation dental restorative materials (e.g., composite resins, alloys, ceramics, smart materials, sealants) as a high-impact use case in biomedical innovation, we present a novel translational architecture that embeds three foundational constructs into the engineering of biomedical digital twins: Fit-for-Purpose (FFP) for scientific and mechanobiological validation, Fit-for-Implementation (FFI) for real-world deployment readiness, and Engineering Readiness Levels (ERLs) for structured decision-making and maturity assessment across the Total Product Lifecycle (TPLC). FiDTs evolve digital twins into dynamic, regulatory-aware, and context-sensitive ecosystems, capable of simulating material–tissue interactions, accelerating preclinical validation, informing adaptive trial design, and integrating with electronic health infrastructure. We introduce the Multi-Participatory Twin Engineering Model (MP-TwinEM) to anchor stakeholder co-development from ideation to implementation to ensure co-development with clinicians, regulators, payers, and patients from the outset. This paper outlines a scalable, modular framework that harmonizes simulation fidelity with deployment feasibility. It includes actionable tools for risk mitigation, accessibility modeling, and global context adaptability, enabling DT systems that are not only predictive, but also trusted, scalable, and ethically grounded. In advancing the FiDT concept, we offer a visionary yet pragmatic foundation for next-generation DT engineering, one that supports translational science mandates, anticipates regulatory evaluation, and accelerates the delivery of accessible, sustainable innovations in oral health and beyond.