FEDERATED GENERATIVE AI FRAMEWORK FOR PERSONALIZED DENTAL CANCER THERAPY A MULTI-MODEL DECISION SUPPORT APPROACH

Abstract

Dental cancer poses a major global challenge due to late diagnosis, biological heterogeneity, and variable response. Traditional planning depends on clinician experience and generic protocols This study proposes a novel Generative AI-enabled precision oncology framework for multi-modal clinical decision support in dental cancer. The proposed framework integrates heterogeneous patient information including radiological imaging, genomic sequencing, histopathological slides, electronic health records, and therapeutic response patterns. The framework combines Vision Transformers (ViTs), Graph Neural Networks (GNNs), Generative Adversarial Networks (GANs), Denoising Diffusion Probabilistic Models (DDPMs), Reinforcement Learning (RL), and Federated Learning (FL) to generate individualized treatment recommendations. Explainable Artificial Intelligence (XAI) mechanisms using SHAP provide transparent reasoning behind AI-driven clinical decisions. Experimental evaluation on 1,847 dental cancer cases demonstrated a clinical concordance of 94.3% with expert oncologist recommendations while reducing treatment planning time by 67%. The proposed multi-modal framework achieved superior conformity indices and enhanced organ-at-risk protection during radiotherapy planning. Furthermore, federated learning enabled privacy-preserving collaborative model development across multiple institutions with minimal performance degradation.