# Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
###### Abstract  
Digital society strategies in healthcare include the rapid development of digital twins (DTs) for patients and human organs in medical research and the use of artificial intelligence (AI) in clinical practice to develop effective treatments in a cheaper, quicker, and more effective manner. This is facilitated by the availability of large historical datasets from previous clinical trials and other real-world data sources (e.g., patient biometrics collected from wearable devices). DTs can use AI models to create predictions of future health outcomes for an individual patient in the form of an AI-generated digital twin to support the rapid assessment of in silico intervention strategies. DTs are gaining the ability to update in real time in relation to their corresponding physical patients and connect to multiple diagnostic and therapeutic devices. Support for this form of personalized medicine is necessary due to the complex technological challenges, regulatory perspectives, and complex issues of security and trust in this approach. The challenge is also to combine different datasets and omics to quickly interpret large datasets in order to generate health and disease indicators and to improve sampling and longitudinal analysis. It is possible to improve patient care through various means (simulated clinical trials, disease prediction, the remote monitoring of apatient's condition, treatment progress, and adjustments to the treatment plan), especially in the environments 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 through 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. artificial intelligence; machine learning; medical engineering; personalized medicine; digital twin; DT; AI-based DT; omics +
Footnote †: journal:  
2 Development of the digital twin concept (1990s-2000s);
3 Technological advances (2010s);
4 Modern applications (2020s) [2; 3; 4]. The concept of using computers to simulate physical systems began in the 1960s and 1970s with the development of computer-aided design (CAD) systems that allowed the creation of digital models of physical objects. The early concept of DTs is believed to be partially related to NASA's use of pairing technology to create mirror systems during the Apollo missions, which created physical and digital replicas of spacecraft on Earth, enabling the monitoring and troubleshooting of space missions. In the 1990s, the development of product lifecycle management (PLM) systems integrated product lifecycle data and processes into digital models for design, manufacturing, and maintenance. The term \"digital twin\" was first used by Dr. [PERSON] at the University of Michigan in 2002 as a virtual representation--a real-time digital equivalent of a physical object or process. The development of the Internet of Things in the 2010s extended the capabilities of DTs to include sensors embedded in physical objects. These enabled real-time data collection and communication with their digital counterparts. During this period, digital twins have been implemented in many industries, including healthcare, as discussed later. Subsequently, the integration of AI and machine learning (ML) with DTs has enabled more sophisticated analysis and predictive capabilities. As a result, DTs have evolved from static models to dynamic systems capable of simulating complex behaviours and scenarios. In 2020, the Digital Twin Consortium was established to promote best practices and standardize digital twin technologies across technology companies, government agencies, and academic institutions. The further development of AI-based DTs by 2030 will be based on emerging technologies, including those based on 6G (Figure 1) [4; 5; 6].