Diagram showing the lifecycle of a cancer patient digital twin (CPDT). 1. The multiscale/multimodal patient data is used to train models. 2. This creates the CPDT that can be explored with advanced computing. 3. Through model inference, researchers can make CPDT predictions. 4. That information can be shared with clinicians who can make care decisions based on individualized patient data. 5. Accumulating this data and knowledge can inform and learn from population studies, clinical trials, and other basic science research from digital twin patient cohorts. 6. Finally this in turn leads to the first step of gathering multiscale/multimodal data from a patient.
The CPDT is envisioned to have a real-time, dynamic life cycle with multiscale/multimodal data harmonization and integrated model training and inference. Advanced computing will create and explore mathematical, statistical, mechanistic, and AI models.

Courtesy of Nature Medicine