Digital Twins and Predictive Patient Modeling
A Digital Twin is a real-time virtual counterpart of a physical patient. In 2026, these are used to move from reactive medicine to predictive, personalized care.
Radiomics Integration: AI algorithms extract thousands of quantitative features from medical images—texture, shape, and pixel intensity—that are invisible to the human eye. These data points are fed into the digital twin to predict how a specific patient’s tumor will respond to chemotherapy.
Real-Time Monitoring: Digital twins are synced with wearable sensors. If a patient with a heart implant experiences an arrhythmia, the digital twin simulates the event, allowing doctors to visualize the internal mechanical stress and adjust treatment remotely.
Biometric Synchronization: These models allow for the "virtual testing" of medical devices, such as pacemakers, ensuring the device settings are optimized for the patient's unique anatomy before implantatio