## 4 Analyses of the Applications of Digital Twin Models
### Medicine
#### 4.2.1 Application and Adoption of Digital Twin in Medicine
The company Myontec, through an application [83], obtains information from patients by wearing pants that transmit muscle activity and health status in a digital environment, where the muscles behavior threshold is computed, realized, and analyzed post-exercise [80]. Physiotherapy is another field of DT application. For example, for people who have had accidents, real-time data can be captured to determine if they are responding to treatment by monitoring their health. Time can be saved and, at the same time, expensive treatments that will not have a positive impact on the patient can be avoided. Another representative example in the area of personalized medicine is organ transplantation, where the DT can predict the probability of acceptance of a new organ by the body. The development of 5G technology together with DT can also come to the aid of military teams that send data about the state of health of soldiers, examining the methods of intervention in which to act through sensors embedded in clothes [80]. In ref. [84], [PERSON] et al., conducted a state-of-the-art analysis on the impact of Digital Twins in medicine. This technology shows promising advancements in patient health monitoring, accurate diagnosis, and precise treatment. For instance, in the treatment of malignant tumors, a Digital Twin was created for the patient, which utilized genotype data and applied various computational algorithms and bioinformatics principles to predict the effects of anticancer drugs. Consequently, Digital Twin assists medical staff by suggesting the most suitable treatment options and benefits patients by improving survival rates and quality of life. However, the study also identified several limitations and highlighted ethical and technological risks associated with the use of Digital Twins in medicine. These include concerns about data confidentiality and unanticipated issues in the physical space during surgical interventions that were not detected in the digital model. In ref. [80], [PERSON] et al., emphasized the benefits of Digital Twins in enhancing hospital operations. Using Digital Twin technology, predictions were made to optimize both costs and resource utilization in a radiology department, including work hours, CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) scan times, and patient waiting periods. For resource management at the hospital or departmental level, various test scenarios and simulations can be performed to improve planning and organization. However, one of the weaknesses identified in [80] is the potential for errors in medical devices transmitting data to physical assets. These errors can result in the Digital Twin model being trained with inaccurate data, leading to incorrect diagnoses and inappropriate treatments. In ref. [85], [PERSON] et al., developed a Digital Twin for breast cancer detection and monitoring, as well as predicting medical parameters. Utilizing datasets, Artificial Intelligence, and various machine learning techniques, they proposed creating robust prediction models based on data from medical consultations and blood tests. This approach allows for the simulation of breast cancer progression to identify potential complications. The discussion in [85] also addressed limitations related to patient data management, such as ensuring confidentiality, data encryption, compliance with GDPR and ISO 27001 standards [86], and monitoring platform access. Another challenge involves the need for large datasets for model training, model accuracy, and result interpretation by non-experts. In ref. [87], [PERSON] developed a Digital Twin model of the cornea to identify pathologies such as keratocous. This approach utilized in silico modeling and simulation, providing an efficient method for the identification, diagnosis, and simulation of surgical interventions. Additionally, the evolution of corneal parameters was demonstrated through simulations of laser-assisted in situ keratomileusis and photorefractive keratectomy surgeries.