Forecasting disease progression
Patient digital twins: simulating future health for better care and research.
28 October 2026
A practical foundation for precision medicine
Think of a patient digital twin as a living, evolving digital model of a real person. By pulling together their health records, medical images, lab results, medications and even day-to-day signals like activity and vitals, these models can help forecast how a disease might progress, compare different treatment options, and support more personalized care. They can also help researchers create virtual control groups and design better clinical trials.
Key takeaways
- How patient digital twins combine medical images, electronic health records, laboratory values, medications and other data.
- How evolving virtual patient models can forecast risk, disease progression and treatment response under different scenarios.
- The role of multimodal AI, probabilistic models and large language models in generating future clinical trajectories.
- How virtual controls, protocol simulation and individual treatment-effect estimation can support clinical-trial optimization.
- A practical enterprise roadmap spanning use-case selection, data foundations, model validation, workflow integration and governance.
- The validation, regulatory, ethical and reimbursement considerations that will influence adoption at scale.