Redefining the Patient in the Digital Twin Age: A Scoping and Conceptual Model for Flexible, Ethical, and Inclusive Healthcare Systems

The emergence of the Digital Twin (DT) in healthcare signifies a revolutionary change in how patients are represented, managed, and governed within AI-driven systems. Traditionally, healthcare relied on episodic data and static diagnoses; now, the Digital Twin develops a dynamic, continuously learning digital replica of the patient—merging biological identity, data infrastructure, and algorithmic analysis. Although increasingly important in precision medicine and clinical simulations, the theoretical foundations and ethical oversight of Digital Twins are still underdeveloped. This paper provides a scoping review and conceptual integration of 76 peer-reviewed studies (2000–2025) that define the Digital Twin as both a technological system and a cyber-physical entity. Using the PRISMA-ScR methodology, the review identifies key gaps in knowledge, governance, and inclusivity across three intersecting areas—conceptual/theoretical, empirical/technical, and policy/ethical research. Six themes emerged: trust and transparency, cultural inclusivity, data agency, participatory governance, AI personalization, and moral regulation. Building on these findings, the study proposes a three-dimensional framework that redefines the patient concept in the Digital Twin era along the axes of identity, agency, and participation, and adaptive governance. This framework underscores that ethical AI in healthcare requires reflexive, participatory, and equitable governance models that can evolve alongside technology. By viewing the Digital Twin as a socio-technical ecosystem rather than just a computational tool, this paper promotes a policy approach for responsible, transparent, and human-centered innovation in the field of intelligent medicine.