Risk Assessment for Digital Twins Applied in Systems Medicine

The increasing adoption of the Digital Twin technology in medicine offers immense potential for improved patient care, personalized treatments, and enhanced clinical outcomes. However, along with these advancements, several risks and challenges arise that need to be addressed to ensure the safe and effective implementation of digital twin systems in healthcare. Ensuring the accuracy and reliability of computational models is essential for effective Digital Twin applications in medicine. Rigorous validation and verification processes, including model calibration, sensitivity analysis, and comparison with real-world data are necessary to establish confidence in the models' performance. Regular updates and maintenance of the models should be conducted to account for evolving medical knowledge and technological advancements. The use of Digital Twin technology in Systems Medicine enables the development of personalized diagnostic tools and treatment strategies. Through the fusion of patient-specific data and computational models, healthcare professionals can gain valuable insights into disease mechanisms, identify biomarkers, and make informed decisions regarding patient care. This personalized approach has the potential to revolutionize the field of precision medicine. The paper highlights the need for a comprehensive risk management approach, encompassing data security, model validation, regulatory compliance, stakeholder collaboration, and ongoing monitoring. By proactively addressing these risks, the healthcare industry can harness the full potential of Digital Twin technology while ensuring patient safety and privacy.