Abstract
The development of lung digital twins (DTs) represents a significant advance in personalized medicine, providing a virtual framework that replicates the structure, function, and pathology of the respiratory system in an individualized manner. DTs integrate clinical data, high-resolution images, and mathematical models to simulate respiratory mechanics, gas diffusion, and fluid dynamics in real time. This technology improves diagnosis, treatment planning, and disease progression monitoring. One of the key applications of lung DTs is the ability to simulate patient-specific response to treatments and predict outcomes, allowing for personalized therapies. Despite advances, the implementation of DTs in clinical practice faces challenges related to data integration, computational efficiency, and ethical considerations regarding data privacy. Nevertheless, lung DTs offer clear promise for improving precision medicine, optimizing patient care, and improving clinical outcomes.