From Static Records to Virtual Digital Patients: An Artificial Intelligence‐Driven Framework for Medical Data Assetization and Governance

Healthcare data remain difficult to use because of fragmentation, sensitivity, and overlapping rights and responsibilities. A virtual digital patient links a longitudinal representation of an individual patient with modeling capabilities for specified clinical or research questions. This article proposes a conceptual socio‐technical framework for governing how virtual digital patients are created, accessed, used, and updated. A narrative synthesis of selected policy, modeling, interoperability, and health artificial intelligence literature informs four interdependent layers: data and representation; artificial intelligence modeling and simulation; governance and control; and clinical and external use. The framework connects standardized longitudinal representations, knowledge‐supported reasoning, disease‐specific simulation, authorization, output review, lifecycle management, and accountability. It distinguishes data resources, model services, operational capabilities, and potentially recognizable assets; technical functionality alone does not establish asset status. An illustrative oncology trial‐design scenario shows how purpose‐limited requests could produce bounded, reviewed outputs without transferring raw patient records. A staged validation agenda addresses data quality, model performance, workflow safety, clinical utility, and governance controls. The article reports neither a new algorithm nor a deployed or clinically validated system. Its contribution is an integrated reference architecture for evaluating how patient‐specific modeling, institutional stewardship, and value return may be aligned. Disease‐specific implementation and empirical evaluation are required before clinical or commercial claims can be established.