This study evaluates the performance of multiple deep learning models for automatic recognition of museum artifacts using image frames extracted from real-world video footage. A comparative analysis is conducted across eight state-of-the-art architectures—MobileNetV3, ResNetV2, EfficientNetV2, You Only Look Once v8 (YOLOv8), Visual Geometry Group 16 (VGG16), ConvNeXtTiny, SwinV2-Base, and Dual Attention Vision Transformer (DaViT)—on a custom dataset collected in an actual museum environment. The dataset comprises labeled video frames categorized by artifact type and is used to train and test models for both classification and object detection tasks. Results indicate that YOLOv8, MobileNetV3, and DaViT achieve superior performance for real-time mobile and augmented reality (AR) applications, while ResNetV2 and SwinV2-Base provide high classification accuracy suitable for archival and cataloging systems. This work offers practical guidance on dataset design, model choice, and deployment strategies for artificial intelligence (AI)-powered cultural heritage technologies.

Active use of immersive technologies in medicine. The development of IT leads to the emergence of new digital products that are actively used in medicine. Further development leads to the transfer of some medical services to the metaverse – the concept of Metahospital is formed. This article provides a systematic review of research to determine the prevalence and current state of the use of immersive technologies for the development of digital health products. Studies from leading scientific databases were reviewed. The results show that research interest in the use of immersive technologies in medicine has increased significantly over the past ten years, peaking during the pandemic. Immersive technologies are the most common types of technologies used in medicine for training, practical training, telemedicine, and diagnostics. In addition, our own software product is described – a coronary artery stenting simulator using virtual reality technology. A description of the model and the results of a survey on the effectiveness of using immersive technologies among medical school students are provided.