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Nowadays, “personalized medicine” is starting to replace the current “one size fits all” approach.It is crucial to understand the aerosol drug dynamics better and develop a revolutionary patient-specific pulmonary disease treatment platform and to improve therapeutic outcomes. In this paper, the discussion paves the way to achieve personalized pulmonary healthcare by introducing the digital twin and lung aerosol dynamics modeling platform as a noninvasive tool for individualized treatment planning. Challenges and breakthroughs are also discussed as the future research directions. Lung cancer, a highly fatal disease, killed 1.6 million of people in 2012 and accounted for billions in health care costs worldwide. Given the fact that the existing lung cancer treatments have major portions of the aggressive medicine deposit on healthy tissue, there is an urgent need to develop a revolutionary patient-specific pulmonary drug delivery method to effectively target localized lung tumors and thereby protect patients from undesired side effects, reduce the healthcare cost, as well as enhance the therapeutic outcomes. Enlightened by the idea of personalized health care and based on the trends that personalized medicine is starting to replace the current “one size fits all” approach to medical treatment, the goal is to develop an effective personalized pulmonary healthcare planning tool to deliver the right dose of the right drug at the right time and location to the specified patient with a generalized pulmonary drug delivery modality to treat lung cancer. An example of personalized pulmonary healthcare planning is the targeted pulmonary drug delivery methodology [1,2]. Indeed, conventional pulmonary drug delivery devices still have poor efficiencies (<25%) for delivering drugs to the lung tumor sites. Major portions of the aggressive medicine deposit on healthy tissue (Figure 1a). In contrast, by designing a patient-specific targeted drug delivery method (Figure 1b), clinical doctors will be able to increase the local deposition efficiency of drugs to 90%. However, traditional in vitro and in vivo studies are limited and not sufficient for the personalized treatment plan development purpose. Specifically, due to the invasive nature and imaging limitations, animal studies and clinical tests are lack of operational flexibilities and will not be able to provide insightful high-resolution patient-specific data. Therefore, alternative methods should be developed to conquer these bottlenecks. Models based on the computational fluid-particle

Create the Individualized Digital Twin for Noninvasive Precise Pulmonary Healthcare