## 6 Summary and Conclusions  
Progress in artificial intelligence (AI), the Internet of Things (IoT), and cloud computing has provided a robust foundation for the rapid evolution of digital twin (DT) solutions. These technological advancements have paved the way for DT applications to expand across numerous industries, including healthcare. With unprecedented pressure on the healthcare sector, providers can deploy DT solutions to better meet the standard of high-quality patient care. Healthcare executives expect to increase their investment in DT to improve performance, highlight areas of improvement, enable customization and personalization of medicines and diagnostics, and allow the development of new drugs and devices. Digital twin technology provides a platform for exploring innovations, preparing for various future scenarios, and testing countless \"what-if\" possibilities. Its potential is vast, especially in areas such as surgical training and the simulation of medical devices and treatments. For instance, when a DT models the human heart, it enables doctors and clinicians to pre-simulate the most effective therapies, potentially avoiding unnecessary procedures. In the fields of medicine and public health, digital twin (DT) technology has the potential to transform traditional electronic health and medical records. By enhancing these systems, DTs can improve both individual records and large-scale population datasets, ushering in an era of greater precision and accuracy in healthcare. While the adoption of DTs in healthcare is advancing, this rapid growth brings both opportunities and risks, as the technology is still in its early stages. Consequently, some healthcare providers are exploring DTs with select partners. In the near future, organizations will be able to collaborate within DT environments, sharing designs, insights, and information across systems, testing innovations virtually, and delivering care in previously unimaginable ways. DT technology is propelling a significant digital revolution, promising unprecedented efficiencies. In the future, DT applications in healthcare will expand, incorporating other technologies like augmented reality (AR) for immersive experiences and artificial intelligence (AI) for enhanced insights and analytics. Such advancements reduce the need to verify results against physical counterparts, leading to new opportunities for DT applications in healthcare operations. However, DT technology also presents challenges, including the risk of increasing the 'digital divide.' Additionally, it could result in discriminatory segmentation based on patterns identified across a population of digital twins. To address these issues, governance structures are essential to protect individual rights, ensure data privacy, and promote fairness and transparency in data use and in the equitable distribution of benefits at both individual and societal levels. Despite the potential, implementing medical digital twins (MDTs) faces several obstacles. Chief among these are ensuring data privacy and security, addressing ethical concerns around patient ownership and control of health data, and smoothly integrating MDTs into existing healthcare systems [75; 76]. These challenges require careful consideration and resolution. Nevertheless, the potential of MDTs to revolutionize personalized medicine is undeniable. With these virtual models, healthcare can be tailored to meet each patient's unique needs, leading to better health outcomes and a healthier future for society. Currently, DT technology is not yet widely used in clinical settings. Although there are ongoing efforts to develop DTs representing physiological functions, individual organs, or complete models, the practical use of digital patient twins in daily medical practice remains limited [77]. There is significant potential for hospitals and laboratories to adopt this technology more extensively, which would enhance digital simulations, optimize clinical processes, and improve the quality of medical care overall.