International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Digital twin technology has rapidly evolved into a foundational pillar of modern engineering practice. By creating high-fidelity, data-driven virtual replicas of physical systems, digital twins enable continuous monitoring, predictive analytics, and advanced optimization throughout the lifecycle of assets. This extended article expands on the conceptual, technical, and practical dimensions of digital twins, providing deeper insights into architectures, data frameworks, integration strategies, real-world case studies, economic implications, and emerging research directions.

Over recent decades, healthcare has undergone transformation through digitalization, and systematic data exchange. These developments have contributed to the emergence of Healthcare 4.0 and have changed the organization, delivery, and monitoring of healthcare services. Modern healthcare systems increasingly rely on interconnected technologies that link patient-based and hospital-based components into broader digital ecosystems, supporting communication, structured data sharing, and more continuous healthcare delivery. Wearable and sensor-based technologies have expanded home-based patient monitoring by enabling the collection and transmission of health-related data outside traditional clinical settings. This data may be shared with healthcare professionals and incorporated into the electronic health record (EHR), a one of the key components of modern eHealth infrastructure. However, clinical usefulness depends on effective integration, interoperability, and interpretation within healthcare information systems. Telemedicine platforms enable remote communication between healthcare professionals and patients and may provide access to selected clinical data in real time. This concept can support individualized counselling, continuity of care, and adherence to therapeutic recommendations. Nevertheless, telemedicine should not be understood as an isolated service, but as part of a broader digital healthcare ecosystem. Artificial intelligence (AI) has further expanded the potential applications of telemedicine across medical specialties and may contribute to improved healthcare efficiency. This article provides an overview of current eHealth systems and telemedicine, with attention to their integration into digital health infrastructure and future perspectives in the transition from Healthcare 4.0 toward Healthcare 5.0.

The application of artificial intelligence (AI) in medicine has emerged as a topic of global interest. Since the introduction of the term in the mid of 20th century, there has been considerable progress in the development of computer systems, leading into their integration into healthcare. At present, AI is increasingly being adopted across a growing number of medical specialties, where it contributes not only to diagnostic processes but also to the selection of appropriate treatments and the prediction of patient outcomes. AI prediction is particularly useful in the management of chronic conditions. Furthermore, AI demonstrates significant potential to expedite routine procedures, thereby allowing healthcare professionals to dedicate more time to cognitively demanding tasks, in which AI systems continue to present certain limitations. Nevertheless, despite notable advancements in recent years, several challenges must still be addressed in future research. These include the formulation of standards and guidelines for AI implementation, the assurance of cybersecurity to safeguard sensitive data, and the continuous education and training of healthcare practitioners. In conclusion, AI holds considerable promise for enhancing the quality and efficiency of healthcare delivery. Its role is not to replace human professionals, but rather to augment their performance and optimize the use of their time and expertise.

Over the past four decades, the field of medical robotics has achieved remarkable advancements, revolutionizing various medical disciplines. The widespread adoption of robotic platforms across various medical disciplines has been remarkable. Currently, these devices play a crucial role in performing minimally invasive surgical procedures with enhanced precision, resulting in reduced hospitalization and increased safety for physicians. Beyond surgical applications, medical robots are increasingly proving their worth in performing routine tasks, thereby enabling less invasive and more informative diagnostic and therapeutic procedures. Furthermore, the integration of artificial intelligence (AI) holds great promise for developing new systems with higher autonomy levels and improving existing ones. This paper describes a short view on history of the development of robotics from the beginning to the current state along with a brief outline of the future direction of development of medical robotics. At first describes the process of development from the first medical robotic device prototype to the modern minimally invasive surgical devices currently used in medical practice. Then it presents expansion of robotics into other medical fields, including ophthalmology, gastroenterology, cardiology and cardiac surgery, physiotherapy, or radiology. Finally, it describes some perspectives for future development in medical robotics, as well as the obstacles, that need to be overcome to improve the efficiency and level of autonomy, in the systems.

Approaches to machine and equipment maintenance based on data analytics and artificial intelligence are trending in modern manufacturing. These methods are used to predict the remaining useful life (RUL) of equipment and thus enable forward maintenance planning. However, for predictive maintenance systems, it is also necessary to detect anomalies in operation and classify the occurring errors. Classical approaches of supervised machine learning are often in this case unusable because those methods require a large amount of run-to-failure data (R2F), which is often not possible to collect due to the undesirable character of failure states in the manufacturing process. The paper presents and tests several methods of detecting device fault states using an autoencoder network, which offers a beneficial solution in the case of the unavailability of R2F data in the system.

The article focuses on the definition of industrial business risks that are associated with risk management. Following the description of the risks, the article then focuses on the elimination of the hazard or the reduction of each of the two criteria that determine the risk in question, such as the severity of the damage caused by the hazard and the likelihood that the damage will occur, separately or simultaneously, are two ways to achieve the purpose of risk reduction. The article is highly relevant to the implementation and development of technical systems and equipment.

Artificial intelligence become more widespread in all manufacturing subjects. In manufacturing artificial intelligence deals with such tasks as quality control, robot navigation, computer vision, processes controlling, etc. The area of maintenance in machining is a great prospect for implementing artificial intelligence tools for analysis, prediction of monitored parameters, optimization, and improvement of the quality of the maintenance process. In particular, the article refers to predictive maintenance as a modern trend in mechanical engineering. In this article, a quick review of using methods of artificial intelligence and predictive analytics in maintenance and one p ractical implementation case of NAR network for time-series prediction was provided.

This article describes the actual trends and applications in industry where artificial intelligence models are deployed. This paper provides a more detailed description of the principles and methods of deploying models in the field of quality evaluation in industry and also in the areas of predictive maintenance and data analytics in the manufacturing process. Computer vision is increasingly coming to the fore due to its wide range of applications – object detection, categorisation of objects, reading QR codes and others. The area of predictive maintenance is important in terms of reducing downtime and saving costs for machine components. Models designed for data analytics, in turn, help to optimize the parameters of the production process so that the desired parameter is maximized or its optimal value is achieved.