Recently, during the COVID-19 pandemic, distance education became mainstream. Many students were not prepared for this situation—they lacked equipment or were not even connected to the Internet. Schools and government institutions had to react quickly to allow students to learn remotely. They had to provide students with equipment (e.g., computers, tablets, and goggles) but also provide them with access to the Internet and other necessary tools. On the other hand, teachers were trying to adopt new technologies in the teaching process to enable more interactivity, mitigate feelings of isolation and disconnection, and enhance student engagement. New technologies, including Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Extended Reality (XR, so-called Metaverse), Big Data, Blockchain, and Free Space Optics (FSO) changed learning, teaching, and assessing. Despite that, some tools were implemented fast, and the COVID-19 pandemic was the trigger for this process; most of these technologies will be used further, even in classroom teaching in both schools and universities. This paper presents a concise review of the emerging technologies applied in distance education. The main emphasis was placed on their influence on the efficiency of the learning process and their psychological impact on users. It turned out that both students and teachers were satisfied with remote learning, while in the case of undergraduate children and high-school students, parents very often expressed their dissatisfaction. The limitation of the availability of remote learning is related to access to stable Internet and computer equipment, which turned out to be a rarity. In the current social context, the obtained results provided valuable insights into factors affecting the acceptance and emerging technologies applied in distance education. Finally, this paper suggests a research direction for the development of effective remote learning techniques.

Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.

The dynamic COVID-19 pandemic has destabilized education and forced academic centers to explore non-traditional teaching modalities. A key challenge this creates is in reconciling the fact that hands-on time in lab settings has been shown to increase student understanding and peak their interests. Traditional visualization methods are already limited and topics such as 3D molecular structures remain difficult to understand. This is where advances in Information and Communication Technologies (ICT), including remote meetings, Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (XR, so-called Metaverse) offer vast potential to revolutionize the education landscape. Specifically, how MR merges real and virtual life in a uniquely promising way and offers opportunities for entirely new educational applications. In this paper, we briefly overview and report our initial experience using MR to teach medical and pharmacy students. We also explore the future usefulness of MR in pharmacy education. MR mimics real-world experiences both in distance education and traditional laboratory classes. We also propose ICT-based systems designed to run on the Microsoft HoloLens2 MR goggles and can be successfully applied in medical and pharmacy coursework. The models were developed and implemented in Autodesk Maya and exported to Unity. Our findings demonstrate that MR-based solutions can be an excellent alternative to traditional classes, notably in medicine, anatomy, organic chemistry, and biochemistry (especially 3D molecular structures), in both remote and traditional in-person teaching modalities. MR therefore has the potential to become an integral part of medical education in both remote learning and in-person study.

Tooth anatomy is fundamental knowledge used in everyday dental practice to reconstruct the occlusal surface during cavity fillings. The main objective of this project was to evaluate the suitability of two types of anatomical tooth reference models used to support reconstruction of the occlusal anatomy of the teeth: (1) a three‐dimensional (3D)‐printed model and (2) a model displayed in augmented reality (AR) using Microsoft HoloLens. The secondary objective was to evaluate three aspects impacting the outcome: clinical experience, comfort of work, and other variables. The tertiary objective was to evaluate the usefulness of AR in dental education. Anatomical models of crowns of three different molars were made using cone beam computed tomography image segmentation, printed with a stereolithographic 3D‐printer, and then displayed in the HoloLens. Each participant reconstructed the occlusal anatomy of three teeth. One without any reference materials and two with an anatomical reference model, either 3D‐printed or holographic. The reconstruction work was followed by the completion of an evaluation questionnaire. The maximum Hausdorff distances (Hmax) between the superimposed images of the specimens after the procedures and the anatomical models were then calculated. The results showed that the most accurate but slowest reconstruction was achieved with the use of 3D‐printed reference models and that the results were not affected by other aspects considered. For this method, the Hmax was observed to be 630 μm (p = 0.004). It was concluded that while AR models can be helpful in dental anatomy education, they are not suitable replacements for physical models.

Diabetic retinopathy (DR) is a diabetes disorder that disturbs human vision. It starts due to the damage in the light-sensitive tissues of blood vessels at the retina. In the beginning, DR may show no symptoms or only slight vision issues, but in the long run, it could be a permanent source of impaired vision, simply known as blindness in the advanced as well as in developing nations. This could be prevented if DR is identified early enough, but it can be challenging as we know the disease frequently shows rare signs until it is too late to deliver an effective cure. In our work, we recommend a framework for severity grading and early DR detection through hybrid deep learning Inception-ResNet architecture with smart data preprocessing. Our proposed method is composed of three steps. Firstly, the retinal images are preprocessed with the help of augmentation and intensity normalization. Secondly, the preprocessed images are given to the hybrid Inception-ResNet architecture to extract the vector image features for the categorization of different stages. Lastly, to identify DR and decide its stage (e.g., mild DR, moderate DR, severe DR, or proliferative DR), a classification step is used. The studies and trials have to reveal suitable outcomes when equated with some other previously deployed approaches. However, there are specific constraints in our study that are also discussed and we suggest methods to enhance further research in this field.