Emerging from NASA's spacecraft simulations in the 1960s, digital twin technology has advanced through industrial adoption to spark a healthcare transformation. A digital twin is a dynamic, data-driven virtual counterpart of a physical system, continuously updated through real-time data streams and capable of bidirectional interaction. In medicine, digital twin integrates imaging, biosensors, and computational models to generate patient-specific simulations that support diagnosis, treatment planning, and drug development. Representative applications include cardiac digital twin for predicting arrhythmia treatment outcomes, oncology digital twin for tracking tumor progression and optimizing radiotherapy, and pharmacological digital twin for accelerating drug discovery. Despite rapid progress, major challenges, including interoperability, data privacy, and model fidelity, continue to limit widespread clinical integration. Emerging solutions such as explainable AI, federated learning, and harmonized regulatory frameworks offer promising pathways forward. Looking ahead, advances in multi-organ digital twin, genomics integration, and ethical governance will be essential to ensure that digital twin shifts healthcare from reactive treatment to predictive, preventive, and truly personalized medicine.

Digital twin technology, traditionally used in industry, is increasingly recognized for its potential to enhance educational experiences. This study investigates the application of industrial digital twins (DTs) in education, focusing on how DT models of varying fidelity can support different stages of Bloom's taxonomy in the cognitive domain. We align Bloom's six cognitive stages with educational levels: undergraduate studies for "Remember" and "Understand," master's level for "Apply" and "Analyze," and doctoral level for "Evaluate" and "Create." High-fidelity DTs support advanced learners by replicating physical phenomena, allowing for innovative design and complex experiments. Within this framework, large language models (LLMs) serve as mentors, assessing progress, filling knowledge gaps, and assisting with DT interactions, parameter setting, and debugging. We evaluate the educational impact using the Kirkpatrick Model, examining how each DT model's fidelity influences learning outcomes. This framework helps educators make informed decisions on integrating DTs and LLMs to meet specific learning objectives.

As the manufacturing industry shifts from mass production to mass customization, there is a growing emphasis on adopting agile, resilient, and human-centric methodologies in line with the directives of Industry 5.0. Central to this transformation is the deployment of digital twins, a technology that digitally replicates manufacturing assets to enable enhanced process optimization, predictive maintenance, synthetic data generation, and accelerated customization and prototyping. This chapter delves into the technologies underpinning the creation of digital twins specifically tailored to agile manufacturing scenarios within the realm of robotic automation. It explores the transfer of trained policies and process optimizations from simulated settings to real-world applications through advanced techniques such as domain randomization, domain adaptation, curriculum learning, and model-based system identification. The chapter also examines various industrial manufacturing automation scenarios, including bin-picking, part inspection, and product assembly, under Sim2Real conditions. The performance of digital twin technologies in these scenarios is evaluated using practical metrics including data latency, adaptation rate, simulation fidelity among others reported, providing a comprehensive assessment of their efficacy and potential impact on modern manufacturing processes.

Automated driving systems are an integral part of the automotive industry. Tools such as Robot Operating System and simulators support their development. However, in the end, the developers must test their algorithms on a real vehicle. To better observe the difference between reality and simulation--the reality gap--digital twin technology offers real-time communication between the real vehicle and its model. We present low fidelity digital twin generator and describe situations where automatic generation is preferable to high fidelity simulation. We validated our approach of generating a virtual environment with a vehicle model by replaying the data recorded from the real vehicle.

This study advanced tele-operations in Advanced Air Mobility (AAM) through the creation of a Vehicle Digital Twin (VDT) system for eVTOL aircraft, tailored to enhance remote control safety and efficiency, especially for Beyond Visual Line of Sight (BVLOS) operations. By synergizing digital twin technology with immersive Virtual Reality (VR) interfaces, we notably elevate situational awareness and control precision for remote operators. Our VDT framework integrates immersive tele-operation with a high-fidelity aerodynamic database, essential for authentically simulating flight dynamics and control tactics. At the heart of our methodology lies an eVTOL's high-fidelity digital replica, placed within a simulated reality that accurately reflects physical laws, enabling operators to manage the aircraft via a master-slave dynamic, substantially outperforming traditional 2D interfaces. The architecture of the designed system ensures seamless interaction between the operator, the digital twin, and the actual aircraft, facilitating exact, instantaneous feedback. Experimental assessments, involving propulsion data gathering, simulation database fidelity verification, and tele-operation testing, verify the system's capability in precise control command transmission and maintaining the digital-physical eVTOL synchronization. Our findings underscore the VDT system's potential in augmenting AAM efficiency and safety, paving the way for broader digital twin application in autonomous aerial vehicles.

Digital Twins (DTs) are virtual representations of physical objects or processes that can collect information from the real environment to represent, validate, and replicate the physical twin's present and future behavior. The DTs are becoming increasingly prevalent in a variety of fields, including manufacturing, automobiles, medicine, smart cities, and other related areas. In this paper, we presented a systematic reviews on DTs in the autonomous vehicular industry. We addressed DTs and their essential characteristics, emphasized on accurate data collection, real-time analytics, and efficient simulation capabilities, while highlighting their role in enhancing performance and reliability. Next, we explored the technical challenges and central technologies of DTs. We illustrated the comparison analysis of different methodologies that have been used for autonomous vehicles in smart cities. Finally, we addressed the application challenges and limitations of DTs in the autonomous vehicular industry.

Imagine having a virtual version of yourself, a digital twin, that can help you make better decisions about your health and lifestyle. Sounds like science fiction, right? Well, with the advancements in artificial intelligence (AI) and personalized health monitoring, this concept is becoming a reality. In this article, we'll explore how AI-driven personalized health monitoring is changing the way we approach healthcare and what it means for the future of medicine. A digital twin is a virtual replica of a physical object, system, or even a human being.

The first question pops into our mind is "what is digital twin technology?" A virtual replica of a tangible object is called a "digital twin." It could be anything basic like a piece of furniture or something as complex as an automobile or a manufacturing production line. All the components of the object are simulated by the digital twin to provide a virtual proxy. What advantages do digital twin offer?

Digital Twin technology has been around for decades. The concept is believed to have its origin at NASA when simulations were carried out to bring back the Apollo 13 astronauts. Gartner defines digital twin as a digital representation of a real-world entity or system. Simulations of what-if scenarios can be performed on this digital/virtual copy of the asset in deriving at the next-best action. Machine Learning models can perform predictive and prescriptive analytics on this digital copy which can then be applied back to the actual asset.

As your self-driving car navigates this exhilarating course, decelerating for twists and turns, the stakes are high. That's because on this test track, no variable in any given driving scenario is left unturned, and every variable is repeatably and reliably measured. The mission is to train your car's autonomous driving algorithm so that it makes the optimal decision every time, with no catastrophic errors in the process. This sounds like an impossible feat in the real world, where virtually every variable surrounding a self-driving car is unpredictable. Moreover, accidents during test runs are bound to happen.