# Digital twins: The New Frontier for Personalized Medicine?
## 2 The Digital Human Twin (DHT) Framework  
DHT consists of virtual replicas of an individual which encompasses all the morphological (anatomical), physiological, and-ideally-cognitive properties, to establish an all-round digital representation of the human body. A key feature of the DHT is the bi-directional data flow between the physical entity and its virtual replica, allowing for real-time continuous updating of the virtual model and, conversely, targeted interventions on the physical systems based on predictive simulations performed on DHTs. Due to the complexity of biological organisms, the construction of DHTs requires advanced data analytics and visualization tools for multi-scale biological data including three main domains: (1) so-called multi-omics sciences, such as genomics, transcriptomics, proteomics and metabolomics, (2) morphological (anatomical) data, and (3) biofunctional data (for example, cardiac electrophysiology) (Figure 3). A DHT aims to represent the virtual replica of an individual or patient with its distinctive genotypic and phenotypic signature, ideally accounting also for the complex interplay between organisms and the environment. Figure 2: Graphic representation of all the aspects required for the development of digital twins. The idea of building a well-rounded DHT is still a future prospect, however, many companies and research institutes have been exploring the possibility of building digital copies of body parts or physiological systems aimed at specific applications [8]. These accelerate the paradigm shift towards personalized medicine, helping to optimize the therapeutic approach in a tailored way, to predict the future health needs of individuals and populations, and to plan public health policies and interventions [8]. The development of a DHT requires access to large-scale healthcare data repositories, including genetic data and data from medical records, imaging, histopathology, and other relevant sources, that must be high quality, accurate, and complete [9] (Figure 4). Data are processed through advanced analytics and AI-based techniques, including Deep Learning models, which can enhance the sophistication of the DHT simulations, enabling predictive simulations and decision support tools with a high throughput processing of thousands of disease-relevant variables. Deep learning methods address this challenge by using a computationally convenient representation of non-linear maps via artificial neural networks [8,10]. Figure 4: The development of digital twin requires a huge amount of different data. Figure 3: The correct creation of a digital twin requires multi-omics data. Virtual modeling requires high-performance computing capabilities for processing and analyzing extensive biological data, while maintaining effective predictions of real-time interactions [8; 10]. For this reason, the construction of a digital twin has especially led to growing links between big data companies, which own the computational and storage resources, healthcare companies which provide the data, and biomedical research centers which guide the analysis and interpretation of models [8; 10]. A critical aspect of the implementation of digital twins is the lack of interconnectivity among different systems and the medical devices involved in the DHT framework. The adoption of standardized data representation and exchange protocols facilitates seamless communication and the integration of electronic health records, medical devices, and other healthcare systems [11]. This interoperability enables the exchange of patient-specific information, enhancing the accuracy and completeness of the DHT model [12].