SafeBot4Twin – A Large Language Model-based Cyber-secure chatbot to control Medical Digital Twins – is an innovative project that combines cybersecurity and artificial intelligence to enable safe, user-friendly interaction with Medical Digital Twins (MDTs). Developed by RBF Morph and LivGemini within the Cyber 4.0 ecosystem, the project addresses the dual challenge of protecting sensitive patient data and making advanced simulation tools accessible to non-expert users in healthcare.
Medical Digital Twins—virtual replicas of patient-specific anatomy—are increasingly used in drug delivery and surgical planning. However, their dependence on clinical data makes them prime targets for cyberattacks. SafeBot4Twin tackles this issue by integrating a Large Language Model (LLM)-based chatbot designed to securely interact with these systems using natural language commands.
The chatbot functions as a dual-purpose tool: a cybersecurity assistant capable of conducting vulnerability assessments and providing real-time risk alerts, and a clinical interface that allows healthcare professionals to operate Digital Twins without specialized IT skills. The system ensures encrypted communication, secure user authentication, and compliance with GDPR and HIPAA standards.
SafeBot4Twin will be tested through pilot studies involving RBF Morph’s pharmaceutical applications and LivGemini’s cardiovascular surgical planning tools. These studies aim to validate the chatbot’s effectiveness and advance both platforms from prototype to operational readiness.
SafeBot4Twin introduces a breakthrough in making Digital Twin technology secure and accessible, enhancing both clinical decision-making and patient data protection.
SafeBot4Twin is financed by Cyber 4.0.
