Generative AI for Simulating Personalized Treatment Plans for Dental Cancer Patients

This research presents a relevant framework for a new generative AI based treatment plan for dental patients (cancer) based on the architectures of deep learning methods. Combining Generative Adversarial Networks (GANs) and/or Transformer-based learning with Federated Learning, however, the system generates patient-specific treatment recommendations combining multi-modal clinical data including genomic data, radiological imaging, histopathological and response to therapy. The framework uses collections of diffusion models for the Vietnamization of optimized radiation treatment dosimetry protection, and reinforcement learning calculations for dynamical treatment re-modification. Validation of 1,847 cases of dental cancer was performed obtaining 94.3% concordance with the regulation of the expert oncologist and saving 67% of planning time. Globally, AI has a broad array of applications which can include things like symptom diagnosis, but the Explanable AI features built into the system render easy-to-understand reasoning processes that make clinical decision-making comfortable. Glossagnomics is a new approach to optimize personalized treatment in complex dentosquamous malignancies, taking a great step forward in the precision oncology field, where it can be applied in both data at clinical scales and the level of an individual patient.

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