## 6 Discussion  
According to the analysis presented in this paper, there is a strong upward trend in research interest for Digital Twin, reaching the highest number of articles in the current year--59,717. The steady number of articles over the past three years indicates not only the maturity of the concept and its adoption by large research communities but also a consolidation of research interests followed by a focus on relevant applications. Another relevant result of the analysis is the tendency observed in numerous papers to redefine existing paradigms and support the digital transformation process.  
As a result of the analysis carried out in this paper, some future research directions will be presented regarding the applicability of Digital Twin in the field of medicine, highlighting not only its innovative potential but also the perspectives that can influence its evolution.  
In relation to the applicability of Digital Twins in medicine, a holistic approach to the patient emerges. The systemic approach, where each organ can be represented with the help of a Digital Twin, or each pathology can be associated with a Digital Twin, is emerging as a viable research direction. The functional and structural modeling of each organ with the help of Digital Twin and Systems Medicine offers the possibility of identifying and diagnosing pathologies and providing personalized treatments. Thus, virtual models of patients can be developed by modeling the behavior of different organs or systems by combining engineering and medical knowledge.  
From the perspective of creating a Digital Twin associated with each pathology, a detailed analysis of its evolution can be carried out, and the integration of the Digital Twins associated with them could facilitate the identification of connections, correlations, and dependencies that influence the evolution of the previously represented pathologies. Thus, the behavior of the entire system, as well as a system of systems perspective, can be simulated, facilitating the identification of pathologies and the optimization of treatments.  
One of the major challenges is the application of deep learning in the creation of Digital Twins. To create customized models and ensure the accuracy of the results, it is necessary to use large volumes of data; the process of collecting, processing, and transforming them into information is time- and resource-consuming. Issues related to the confidentiality and security of patient data, and the ethical risks that may arise, can also be discussed.  
Starting from the previously presented models, another challenge could be the creation of a modular architecture that ensures the interoperability, performance, security, and reliability of the Digital Twin-type system with applicability in medicine, especially in ophthalmology. Such an architecture should be capable of integrating real-time data, utilizing sensor networks, and applying advanced analysis and prediction techniques, thereby providing a powerful tool for monitoring and optimizing medical treatments.  
As a result of a holistic approach, the Digital Twin becomes an essential tool for the efficient and sustainable management of diverse systems and infrastructures.