In the recent years, Digital Twin (DT) has gained a remarkable place in the top ten technology trends. But digital twinning alone cannot provide solution to many applications. Hence, the integration of DT with the technologies like Internet of Things (IoT), Artificial Intelligence (AI), Cloud Computing, machine learning (ML), Big Data analytics, and Deep Learning (DL) techniques paves way to new opportunities and provides a solution to many research problems in diverse sectors. The DT provides an accumulation of data between the real and digital system in both paths. This chapter focuses on the definition, architecture, components, and different types of DT. It also emphasizes on the convergence of digital twinning with other technologies for solving many research problems and application areas in medical diagnosis, healthcare, and others. It also highlights on the issues and challenges pertaining to DT and its supporting technologies. Finally, a case study pertaining to manufacturing sector using DT is presented.

Over a past decade, a development of Artificial Intelligent (AI) in medical discipline has been utilized to predict and prescribe drugs on day basis. It has large influences on predicting and diagnosing ailments totally based on the information that have been accrued through an embedded sensor. The sensor collects excessive dimensional facts which have a descriptive clarification on every symptom and diagnostics. It has been stated that more than 75% of clinical executives count on to make investments in digital twin (DT) technological know-how over previous few years. As DT is the replication of any physical/virtual objects like people, manner, and equipment which help to put together a digital transformation of scientific files that are gathered. Due to developments in big data analytics and AI in analyzing records that have been accumulated over a length of time for prediction, DT science is being developed and commercialized aviation and manufacturing processes. Rather than making an attempt to acquire a best duplicate of the human mind, AI structures take advantage of strategies emulating human reasoning as information to grant each helping equipment and higher services. Super intelligence of AI not only performs all physical and manpower-related task but also handles prediction and discovering novel applications and remedies to medical problems as business perspectives. DT is defined as virtual object or computer entity or model that simulates or twinning any real-world things like human or objects. Every DT is linked with a unique digital key that normalizes a bijective relationship with its original. The DT has been used to surveillance the functionalities of physical entities uninterruptedly and make a decision over a problem. This paper portraits digital recreation of healthcare systems like lab, hospital ecosystems, and psychology of a human being and how far it is useful in medical science which in flips hikes revenue and enterprise market of healthcare industry along with a study that focuses on DT applications in medical industries and healthcare applications. Finally, the research has serious diversion into various challenges in medical digitalized system and DT technology.

The use of data is a primary motivator for DT. Real object data, virtual design data, customer data, domain expertise, fusion information, and connections data are all explored in this research. With the introduction of DT, information gathering, processing, fusion, interactivity, iterative optimization, universal, as well as on utilization all become increasingly significant. Seven key principles are offered to help organize and handle DTDs that are generated by these requirements. Using these notions as a guide, related approaches for DTD collecting, storing, communication, connection, fusion, development, and innovation are provided. Finally, the important enabling technologies are explored.

Digital twin (DT) is being used in commerce as precise model software through conception to practice as a result of the advancement of skills such as big data, cloud services and the Internet of Things. Furthermore, modelling is crucial in the health sector, particularly in research on healthcare process design, healthcare allocation of resources and medicinal activity forecasting, among other things. There would be a modern and improved technique to give more rapid and reliable operations by merging DTs and healthcare. Nevertheless, in the period of personalized medicine, how and where to accomplish personalized health monitoring all through the lifetime of older patients, as well as how to combine the healthcare material reality and the digital reality to actualize real smart medicine, remain two major obstacles. One of the main goals of precision medicine is to prevent disease from occurring through the use of inheritances, genomics and smart health monitoring surveillance systems. If the sickness cannot be avoided, it would be handled on an individualized or personalized basis, instead of as a group, as it has been in the past. Furthermore, medical technology using smart medicine in a data-driven service paradigm necessitates a wide range of information, including patient, healthcare, economic and fusion data. The innovative diagnostic provider mode is shifting to one of tailored and sustainable therapy as a result of global warming, and considerably more intelligent health devices and platforms are being created to accomplish secure data-driven smart healthcare employing DTs. The competence to explore, identify, comprehend and assess health information generated from electronic databases, as well as knowledge gained, to appropriately address or address health complications, is characterized as e-health. The Internet seems to have the ability to safeguard users from damage and allow people to actually engage in knowledgeable health-related decision making as a storehouse for health records and e-health analytics. Particularly essential, increased amounts of e-health interaction reduce the danger of receiving erroneous online information.

Numerous scientific approaches on privacy and security within Cloud-based e-health systems are addressed, with a focus on the prospects, advantages and difficulties of putting such methods in place. A potential strategic development is the integration of e-health systems with expert machines such as cloud technology to create smart aims and solutions.

The COVID-19 pandemic is endangering the health and lives of people in 223 nations and territories. When dealing with the Coronavirus, early discovery and isolation are the most important measures to take. X-rays, computed tomography and magnetic resonance imaging can show the presence of Covid-19, making infection detection a cinch. Chest X-rays (CXR) of people infected with Covid-19 are shown to have certain abnormalities and it is one of the frequently used imaging modalities. The digital twin monitors people's data, compares them to documented patterns, and analyses illness symptoms. Furthermore, the data might be used to create a digital modelling of a typical healthy patient, which aids in the definition of new healthy criteria. There are a variety of ways to use deep learning to find diseases in X-rays from previous efforts. Initially, 6,000 chest X-rays were collected from publicly available sources. These pictures will be recognized by a radiologist as evidence of Covid-19 sickness. Approximately 15% of children worldwide are killed by pneumococcal disease. Recognized and sorted pneumonia, healthy, and Covid-19 all come from the modified VGG16 deep learning (DL) technique. The convolutional neural network (CNN) is a deep neural network that incorporates both external and internal characteristics and is used by the identification model to identify pixels. The findings indicate that medical professionals should reconsider the use of X-ray images in the treatment of certain diseases and that more research into evaluating X-ray technology is required. The suggested detection approach performed better when compared to chest X-rays for pneumonia and non-pneumonia conditions.