# Digital twins: The New Frontier for Personalized Medicine?
## 1 Background  
The concept of a digital twin refers to a virtual model of a tangible entity or physical system, which can be used to study, monitor, and optimize the composition and functions of its physical counterpart [1, 2]. DITs can be used to simulate a variety of entities with increasing levels of complexity, ranging from everyday consumer objects to transportation systems, cities, ecosystems, and the human body [3]. This concept was first introduced in 2003 by Dr. [PERSON], a professor at the University of Michigan, who described digital twins as a way to create a \"real-time digital representation of a physical object or process, which allows for simulation, prediction, and optimization\" [4] (Figure 1). Digital twins provide powerful insight into the inner workings of a physical system, allowing for in silico simulations of several real-worldscenarios through the arbitrary manipulation of specific variables and parameters of interest, obtaining accurate predictions of the behavior of the system under different conditions. First conceptualized in the aerospace industry, the digital twin idea has become increasingly popular in the context of Industry 4.0 along with the growing trend of automation and digitization, attracting considerable interest in a variety of sectors, including manufacturing [5], transportation [6,7], and healthcare [3]. In manufacturing, they have demonstrated significant results in predictive maintenance and performance optimization, without tampering with or exhausting the original physical product [5]. In the automotive industry, sensors allow for continuous real-time data detection and interchange with remote control centers, increasing the flexibility and customization of services and user safety [6,7]. In medicine, the most ambitious application of digital twins aims to achieve a complete digital representation of the physical patient (hereinafter referred to Digital Human Twin, DHT) in order to improve the prevention, diagnosis, and treatment of diseases. Despite this long-term perspective, currently available applications mostly concern the simulation of single anatomical structures (tissues or organs), physiological processes, or metabolic interactions [8]. At another level, the evolution of digital twins will concern the simulation of healthcare institutions or processes, aimed at improving organizations' workflow and resource management [8]. These models would obviously benefit, in perspective, from the development of DHTs that represent virtual copies of individual patients and that could be integrated into higher-level representations. In all these settings, digital twin applications could improve the study and monitoring of highly complex systems characterized by many interacting components and thousands of variables that can be difficult to characterize with traditional approaches. With the rise in the Internet of Things paradigm, advancements in medical technology, and the growing popularity of wearable devices for biosignal monitoring and health promotion, there exists a vast amount of data available for the development of digital twins. The construction of a digital twin is based on an intricate constellation of skills, professional figures, and technological devices that make up the digital twin framework (Figure 2). This includes large-scale biological data repositories, cloud storage, and computing; standardized protocols for data acquisition and communication; multidisciplinary collaboration; artificial-intelligence-based algorithms; virtual reality technologies; and robust legal and ethical frameworks. Figure 1: Graphical representation of the development of a digital twin of a physical entity from data collection and integration to perform prediction and establish an appropriate intervention. While the digital twin idea has been extensively studied in manufacturing and engineering, its potential application to the human body to create a DHT is still unfolding with vast open prospects [3].