RBF Morph announces the successful completion of SafeBot4Twin, a research and innovation project aimed at enhancing the security and accessibility of Medical Digital Twins through the integration of Large Language Models (LLMs) and advanced cybersecurity technologies.

The project achieved highly encouraging results, exceeding initial expectations. Its outcomes demonstrate the potential of natural-language interfaces to facilitate secure interaction with complex medical simulation environments, while addressing the critical need to protect sensitive healthcare data.

Coordinated by RBF Morph, with LivGemini as project partner and the collaboration of the University of Rome Tor Vergata, SafeBot4Twin developed an LLM-based chatbot combining cybersecurity monitoring capabilities, including vulnerability assessments and real-time risk alerts, with an intuitive interface for interacting with Medical Digital Twins.

The successful completion of the project represents an important step towards making advanced Digital Twin technologies more secure, accessible and applicable to healthcare, supporting both clinical workflows and the responsible adoption of AI-driven solutions.

SafeBot4Twin was funded by the European Union – Next Generation EU (PNRR), Mission 4, Component 2, Investment 2.3, within the Cyber 4.0 ecosystem.

A video presenting the project and its main achievements is available here:
https://youtu.be/koSJIxq9DAI