Sign languages are critical in conveying meaning by the use of a visual-manual modality and are the primary means of communication of the deaf and hard of hearing with their family members and with the society. With the advances in computer graphics, computer vision, neural networks, and the introduction of new powerful hardware, the research into sign languages has shown a new potential. Novel technologies can help people learn, communicate, interpret, translate, visualize, document, and develop various sign languages and their related skills. This paper reviews the technological advancements applied in sign language recognition, visualization, and synthesis. We defined multiple research questions to identify the underlying technological drivers that strive to improve the challenges in this domain. This study is designed in accordance with the PRISMA methodology. We searched for articles published between 2010 and 2021 in multiple digital libraries ( i.e. , Elsevier, Springer, IEEE, PubMed, and MDPI). To automate the initial steps of PRISMA for identifying potentially relevant articles, duplicate removal and basic screening, we utilized a Natural Language Processing toolkit. Then, we performed a synthesis of the existing body of knowledge and identified the different studies that achieved significant advancements in sign language recognition, visualization, and synthesis. The identified trends based on analysis of almost 2000 papers clearly show that technology developments, especially in image processing and deep learning, are driving new applications and tools that improve the various performance metrics in these sign language-related task. Finally, we identified which techniques and devices contribute to such results and what are the common threads and gaps that would open new research directions in the field.

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson’s disease. The review offers valuable insights and informs the research in DL and SM.

The transformative capabilities of large language models (LLMs) are reshaping educational assessment and question design in higher education. This study proposes a systematic framework for leveraging LLMs to enhance question-centric tasks: aligning exam questions with course objectives, improving clarity and difficulty, and generating new items guided by learning goals. The research spans four university courses—two theory-focused and two application-focused—covering diverse cognitive levels according to Bloom’s taxonomy. A balanced dataset ensures representation of question categories and structures. Three LLM-based agents—VectorRAG, VectorGraphRAG, and a fine-tuned LLM—are developed and evaluated against a meta-evaluator, supervised by human experts, to assess alignment accuracy and explanation quality. Robust analytical methods, including mixed-effects modeling, yield actionable insights for integrating generative AI into university assessment processes. Beyond exam-specific applications, this methodology provides a foundational approach for the broader adoption of AI in post-secondary education, emphasizing fairness, contextual relevance, and collaboration. The findings offer a comprehensive framework for aligning AI-generated content with learning objectives, detailing effective integration strategies, and addressing challenges such as bias and contextual limitations. Overall, this work underscores the potential of generative AI to enhance educational assessment while identifying pathways for responsible implementation.

By improving the quality of life and extending the length of life, Western society is becoming an increasingly ageing population with a higher proportion of seniors. From another point of view, there is a critical shortage of care staff, both in hospitals and for in-home care. Thanks to new technology trends such as Smart Homes and Smart Furniture, there is an opportunity for increased support for seniors by utilizing new technologies. This paper presents the current trends and possibilities in applying smart information and communications technology (ICT) solutions for in-home care concerning diseases in old age. The paper consists of a systematic review according to the PRISMA methodology of the available literature in Web of Science, IEEE Xplore, PubMed, Springer, and the Espacenet patent database. Publications report the usage of some types of artificial intelligence and their implementation and non-intrusive sensing technologies. The patents review identified solutions with a focus on monitoring the state of older adults and mobility improvement. Existing ICT smart solutions must address the following issues: (1) ease-of-use; (2) invisibility and disuse that isolate older adults; (3) privacy and security; (4) affordability of technology in terms of cost; and (5) supporting elderly individuals to stay in their homes or move in different environments independently. There is a significant gap between a large number of scientific publications and commercial solutions. The existing products reflect the specifics of the diseases in a rather wider context instead of the fulfilment of exact needs. It is often stated that such devices can be used across diseases, but the direct connection and benefits for the disease is still rather weak. The challenge remains to tap the existing potential of a large number of innovative ideas on the market and improve the quality of life.

Demographic data suggest a rapid aging trend in the active workforce. The concept of aging at work comes from the urgent requirement to help the aging workforce of the contemporary industries to maintain productivity while achieving a work and private life balance. While there is plenty of research focusing on the aging population, current research activities on policies covering the concept of aging at work are limited and conceptually different. This paper aims to review publications on aging at work, which could lead to the creation of a framework that targets governmental decision-makers, the non-governmental sector, the private sector, and all of those who are responsible for the formulation of policies on aging at work. In August 2019 we searched for peer-reviewed articles in English that were indexed in PubMed, IEEE Xplore, and Springer and published between 2008 and 2019. The keywords included the following phrases: “successful aging at work”, “active aging at work”, “healthy aging at work”, “productive aging at work”, and “older adults at work”. A total of 47,330 publications were found through database searching, and 25,187 publications were screened. Afterwards, 7756 screened publications were excluded from the further analysis, and a total of 17,431 article abstracts were evaluated for inclusion. Finally, further qualitative analysis included 1375 articles, of which about 24 are discussed in this article. The most prominent works suggest policies that encourage life-long learning, and a workforce that comprises both younger and older workers, as well as gradual retirement.

Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat people with skin-related diseases. The proof-of-concept was performed with a static dataset of camera images on a desktop computer. After we validated the approach’s feasibility, we implemented the method in a mobile application that allows for communication between patients, caregivers, and healthcare professionals.