## 5 Results
This architecture offers a flexible, secure, and modular vision that can significantly contribute to the development of personalized medicine. The presentation of these architectures and application frameworks used information extracted from ten sources, highlighting the adaptability of Digital Twins in various industries and their interoperability with other platforms. The modular nature of these architectures allows for security measures to be implemented at each layer or across the entire system, minimizing risks and the probability of cyber-attacks. Procedures can also be introduced to protect the data involved in building models and predicting the behavior of physical assets while monitoring system-level access. For manufacturing and PSS integration, the general objective was to identify methods, tools, and models of Digital Twins that ensure the monitoring of physical-virtual and virtual-physical connections throughout the product life cycle. The study explored the use of Digital Twins from the product design phase, pre-identifying customer requirements, analyzing data based on design parameters, extending through maintenance and fault prediction. The study focused on identifying the concepts, technologies, and applications used in the development of Digital Twins in manufacturing, emphasizing their benefits. The analysis-evaluation-validation process identified articles that presented techniques for verifying the conformity of DT specifications and the ways in which DTs interact with the production environment to optimize process parameters. Technologies that provide accurate predictions and monitor the behavior of physical entities have been developed, facilitating effective feedback mechanisms during evaluation and validation phases. The overall objective of integrating Digital Twin with Cyber-Physical Systems (CPS) was to identify the potential contributions of DT technology. The selected articles provided virtual representations of the physical components of CPSs, including human-robot collaborative assembly operations and remote interactions. These representations facilitated real-time monitoring and analysis based on sensor data, such as manipulating the real robot or checking its movement and calculating safety distances. The findings suggest that Digital Twin technology enhances the simulation of systems, helps identify potential problems, optimizes operations, aids in decision making, and efficiently manages resources. This analysis underscores the pivotal role of Digital Twins in enhancing the functionality and safety of CPSs. In the medical field, the general objective was to create a comprehensive model of the human body using Digital Twin technology to simulate and monitor patients' health statuses. The study identified several aspects highlighting the real-time evolution of pathologies, considering molecular characteristics and the effects of drugs. Notably, articles discussed the development of custom emotion recognition systems through image processing and ECG heart rhythm classification models using machine learning and AI algorithms. The research delineated the stages involved in creating a patient's digital model: data acquisition, processing and prediction, monitoring and correction, and data comparison. Various medical specializations where Digital Twin technology can be applied were identified, such as cardiology (digital heart models and chronic heart failure diagnosis), neurology (monitoring brain activity to prevent depression and neurological disorders), and rheumatology (tracking changes in the bone and muscle systems). Future directions include integrating Systems Medicine to develop a Digital Twin associated with the eye, which could identify potential hereditary pathologies (e.g., glaucoma, cataract, macular degeneration), suggest treatment plans, simulate patient reactions, and assist in medical decision making. This comprehensive approach highlights the transformative potential of Digital Twins in personalized and predictive medicine.