Patient-Centric Digital Twin Framework with Hybrid Knowledge Distillation for Federated Class-Incremental Learning in Precision Medicine

Precision medicine requires patient-centric digital twins that continuously adapt to evolving disease taxonomies while collaborating across distributed health care institutions. However, federated learning in medical environments faces dual heterogeneity challenges: spatial divergence from non-identical patient populations across hospitals and temporal dynamics from continuously emerging disease categories. This paper proposes the digital twin-enabled precision medicine with a hybrid knowledge distillation framework that integrates adaptive patient classification loss, clinical semantic distillation loss, and biomarker attention distillation loss. The adaptive weighting mechanism dynamically adjusts preservation strength for different disease categories by computing gradient magnitudes during training, automatically providing stronger retention for categories exhibiting higher forget ting susceptibility while allowing stable categories to receive proportionally less emphasis. The hybrid approach simultaneously preserves soft-label disease relationships, intermediate convolutional feature patterns, and gradient based adaptive weighting across disease categories. Experimental results demonstrate consistent superiority over baseline methods. On the CIFAR100 benchmark, the frame work achieves 71.05% accuracy, representing a 1.87 per centage point improvement. Medical imaging evaluation on OrganAMNIST, OrganCMNIST, and OrganSMNIST datasets shows 2.18%, 1.92%, and 1.95% accuracy gains, respectively. Validation using authentic clinical laboratory data encounters confirms 2.54% accuracy improvement, establishing practical viability for real-world precision medicine deployment where continuous diagnostic knowledge expansion must occur without compromising patient privacy or historical disease recognition capabilities.