# Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
## 3 Results
### AI-Based DT Clinical Use
Based on the YOLOv8 neural network, this system classifies chest X-rays into five distinct lung disease categories (normal, COVID-19, lung opacity, pneumonia, and tuberculosis) with an average accuracy of 96.8%, a precision of 92%, a recall of 97%, and an F1 score of 94%.This not only allows for the real-time monitoring of lung health through continuous data collection from IoT sensors, facilitating early diagnosis and intervention, but also the automated and objective evaluation of chest X-rays, reducing the dependence on subjective human interpretation and combining multiple data streams for personalized treatment plans [44]. [PERSON] et al. presented the AIWork project approach to using digital twin technology to improve work environments where AI and robots seamlessly collaborate with humans in a human-machine task division across six domains. DTs can help occupational physicians and psychologists create a sustainable work environment, e.g., by monitoring the physical and mental health of hospital staff to predict burnout symptoms. In education, AI4 Work can help maintain the mental health of teachers and students and a sustainable learning environment [45]. [PERSON] et al. showed that most of the research on DTs in healthcare has been carried out since 2018, with significant contributions from the interdisciplinary fields of AI, ML, and data science. It enables data interoperability and privacy protection and primarily represents an opportunity for chronic disease management, predictive analysis, drug discovery, and surgery planning [46]. [PERSON] et al. have shown that sensor-driven digital twins can further extend the capabilities of location-based services (LBS) in smart environments to meet both healthcare needs and patient requirements. The broader use of wearable medical devices, AI-based health analytics, and digital twin applications will help improve personalized healthcare interventions and emergency response capabilities. This demonstrates the potential of mobile DTs with patients [47]. In telemedicine, DTs can be used to deliver personalized care plans and real-time health monitoring to remote patients, ensuring that they receive high-quality care regardless of their location [46; 47]. DTs enable virtual clinical trials, where multiple scenarios can be tested in a model before actual human trials, improving design and reducing risk in developing new therapies. By simulating multiple risk factors and their interactions, digital twins can provide personalized health risk assessments, helping to identify and mitigate potential threats to a patient's health. In post-operative or chronic care settings, DTs can be used to track a patient's rehabilitation progress, providing insight into the recovery process and adjusting care plans as needed to support optimal recovery. These benefits of AI-based DTs will see them dominate the healthcare market, displacing competing solutions [48; 49; 50; 51]. It is possible to improve patient care through various means (simulated clinical trials, disease prediction, the remote monitoring of the patient's condition, treatment progress, treatment plan adjustments), especially in the environment of smart cities and smart territories and through the wider use of 6G, blockchain (and soon maybe quantum cryptography), and the Internet of Things (IoT), as well as in the field of medical technologies, such as multiomics. From a practical point of view, this requires not only efficient validation but also seamless integration with the existing healthcare infrastructure [53; 54; 55; 56; 57; 58]. AI-based DTs in preventive medicine are used to proactively monitor and manage a patient's health, with the goal of preventing diseases before they occur. By continuously analysing real-time data from sources such as wearable devices and electronic health records, DTs can identify early warning signs and subtle changes in a patient's health.