## 4 Analyses of the Applications of Digital Twin Models
### Medicine
#### 4.2.1 Application and Adoption of Digital Twin in Medicine  
One of the fields that benefit more and more from the use of DTs is the medical field through the existence of smart portable devices that monitor people's health in real time and by combining engineering and medical knowledge and integrating data laid the foundations of a connected health system [63]. In ref. [64], the authors envisage DT as a digital model of the dynamic characteristics of the individual, namely the evolution over time of the molecular state, physiological state, and lifestyle. A review of the latest engineering achievements in the Organ-On-a-Chip (OOC) domain was presented in [65; 66]. This is based on the effect of different drugs on human tissues but replaces the need for human testing with DTs to obtain the same information. By combining several OOC technologies, an overview of the entire organism can be generated, a technology known as Body-On-a-Chip (BOC). Along with communication and cybersecurity technologies, Virtual Reality (VR) and Computer-Aided Modeling (CAM) represent essential elements in the field of remote surgery [67; 68; 69]. Through Digital Twins and the implementation of emotion recognition systems, patients' emotions can be identified, and intelligent systems can be built to detect stress and depression in its early stages; thus, medication can be administered in advance. Building ER systems also involves technical limitations, such as limited datasets, high implementation costs, identification of salient features, occlusion and illumination problems, and false classification of emotions. In ref. [70], the authors built a custom Emotion Recognition (ER) system that gathers and processes web camera images. In addition, an end-to-end architecture was proposed by combining the ER system with a Digital Twin with a supporting decision-making role based on emotions. The predicted results can be processed, analyzed, and tested, offering the best-personalized treatment. The Digital Twin concept emerges as a solution aimed at improving life expectancy and reducing healthcare costs, benefiting both patients and healthcare professionals. Serving as a virtual representation of an individual, it utilizes real-time monitored data to reflect the person's current health status. For example, in [71], researchers proposed a model for classifying heart rhythms using Electrocardiogram (ECG) data, employing neural networks and machine learning algorithms to diagnose circulatory system pathologies, particularly heart-related issues. The integration of IoT into daily life through smartphones, smart buildings, and wearable health monitoring devices has significantly enhanced healthcare systems. These advancements enable real-time health monitoring, assisting medical professionals in analyzing health data, identifying disease predispositions, and ultimately reducing mortality rates. Additionally, studies like the one conducted by [PERSON] and [PERSON], structured in three stages, monitor patient health status in real time, allowing doctors to promptly prescribe appropriate treatments and adjust them as needed, thereby improving treatment accuracy and patient outcomes. These algorithms, including MLP(Multilayer Perceptron), LR (Logistic Regression), CNN (Convolutional Neural Network), LSTM (Long-Short-Term Memory network), and SVC (Support Vector Classification), are pivotal in processing and analyzing patient data for various medical applications, such as heart rhythm classification and disease detection. In the processing and prediction phase, patient data acquired from portable sensors are transferred to a cloud database in real time, facilitating predictive analysis using machine learning algorithms. Medical staff utilize the results to provide personalized advice and medication during the monitoring and correction phase, contributing to continuous model improvement. The comparison phase involves real-life scenarios, enabling collaboration among patients with similar conditions to enhance Digital Twins and healthcare systems.