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The concept of 'digital twin' has evolved from a niche engineering tool into a pervasive paradigm across industries. A digital twin is a virtual replica of a physical object, process, or system, continuously updated with real-time data. This dynamic model allows for simulation, prediction, and optimization without interfering with the real-world counterpart. In healthcare, digital twins of human organs are being developed to test surgical procedures or drug responses, potentially reducing the need for animal testing and enabling personalized medicine. However, the fidelity of these models hinges on the quality and volume of data collected, raising concerns about privacy and data governance. Moreover, the computational cost of maintaining high-resolution twins remains substantial, limiting their accessibility. Despite these challenges, proponents argue that digital twins could revolutionize decision-making by offering a sandbox for 'what-if' scenarios, thereby reducing risks and costs in fields ranging from urban planning to aerospace engineering.