Table 1 Challenges identified in creating a digital twin for medicine

(1) Identify and solve difficult scientific problems that arise at different scales (e.g., systems biology, biophysics, the immune system)

(2) Address gaps in modeling (e.g., multiscale hybrid stochastic models, model design that facilitates updates and expansion, reusable models, and model standards)

(3) Develop appropriate collection modalities for patient data (e.g., noninvasive technologies and imaging capabilities)

(4) Develop novel forecasting methods (i.e., learning from successful hurricane forecasting)

(5) Develop data analytics methods for model recalibration from patient measurements

(6) Train a highly educated workforce

(7) Create appropriate funding models for individual medical digital twin projects from conception to prototype, and for larger infrastructure development projects