Use of Digital Twins in Healthcare and Cost Monitoring: A Scoping Review

Digital twins (DTs) are an emerging technological innovation in healthcare, enabling real-time simulation and optimization of clinical and operational processes through the integration of AI, IoT, and data-driven modeling. While initially adopted for clinical prediction and treatment planning, their broader economic implications for healthcare management remain underexplored. This paper presents a scoping review of recent research (2023–2025) examining the cost-effectiveness and economic value of digital twins in healthcare. The review synthesizes evidence from fifteen international studies indexed in the Web of Science database, focusing on both implementation costs and potential financial benefits for healthcare providers and systems. Findings show that DTs are most cost-effective in operational and infrastructural applications such as hospital logistics, energy management, and digital hospital ecosystems where cost reductions are directly measurable through improved resource utilization and process efficiency. In contrast, clinical and population-level twins demonstrate mainly conceptual or long-term economic potential, constrained by limited empirical data and methodological heterogeneity. Overall, the review identifies digital infrastructure maturity, interoperability, and workflow integration as key enablers of financial sustainability. It also highlights the lack of standardized economic evaluation methods, underscoring the need for consistent reporting frameworks to inform evidence-based investment decisions in healthcare digitalization.