Explainable Neuro-Symbolic Ontological Evolution for Anatomical Feature Synthesis in 6G-Enabled Tele-Surgery

This project proposes a new framework for the autonomous robotic surgery medical atlas, focusing on a self-modifying ontological system. Using Neuro-Symbolic (NeSy) integration, this research resolves one of the major challenges relating to anatomical and morphological anomalies in the context of real time tele-surgery. Current deep learning based models provide good capabilities for performing models (as well as understanding the models), unfortunately, they do not provide the required interpretability, and consistent, logical, and incremental/basic reasoning that can hold in a critical medical intervention. This project proposes a system involving the concept of synthesising a new anatomical symbol when a new unforeseen, and, in the context of the symbolic models of the architecture, highly atypical structure of the patient is documented in the context of surgery. The system does this justification to the medical expert in the context of the architecture through the Explainable (and) XAI over the low/or ultra low latency 6G telecommunications. Using the combination of topological data analysis with the Blockchain based Federated Learning, this research guarantees that the surgical intelligence system is both adaptive and dynamic, as well as secure and of a provable (formal) trust across the distributed medical networks.