A Scalable AI Driven, IoT Integrated Cognitive Digital Twin for Multi-Modal Neuro-Oncological Prognostics and Tumor Kinetics Prediction using Enhanced Vision Transformer and XAI

Explainability is built into clinical AI systems: The framework uses Grad-CAM heatmaps and 3D visualization to show doctors why the AI makes specific predictions about tumor location and patient brain states, rather than treating the system as a "black box"—a critical requirement for responsible deployment in healthcare settings.

Real-time personalization enables individualized monitoring: By combining wearable EEG data with MRI scans, the system creates personalized "digital twins" for each patient that continuously adapt to their specific condition, moving beyond one-size-fits-all diagnostic approaches and potentially reducing unnecessary treatments.

High accuracy metrics (94.6% precision, 93.2% recall) suggest clinical viability: These performance numbers indicate the system can reliably detect brain tumors and seizure states, which is essential for responsible AI—systems with poor accuracy could cause patient harm if deployed without sufficient validation.

Multi-modal data integration reduces diagnostic blind spots: By analyzing both electrical brain activity (EEG) and structural imaging (MRI) simultaneously, the framework captures complementary information that neither data source alone could provide, potentially catching cases that single-modality systems might miss.

Predictive tumor growth modeling supports proactive care planning: The ability to forecast volumetric changes in tumors could allow clinicians to intervene earlier and plan treatments more strategically, though responsible deployment would require rigorous validation that predictions actually improve patient outcomes in real-world settings.