Background: The aim of this study was to assess social preferences for two different advanced digital health technologies and investigate the contextual dependency of the preferences. Methods: A cross-sectional online survey was performed among the general population of Hungary aged 40 years and over. Participants were asked to imagine that they needed a total hip replacement surgery and to indicate whether they would prefer a traditional or a robot-assisted (RA) hip surgery. To better understand preferences for the chosen method, the willingness to pay (WTP) method was used. The same assessment was conducted for preferences between a radiologist’s and AI-based image analysis in establishing the radiological diagnosis of a suspected tumour. Respondents’ electronic health literacy was assessed with the eHEALS questionnaire. Descriptive methods were used to assess sample characteristics and differences between subgroups. Associations were investigated with correlation analysis and multiple linear regressions. Results: Altogether, 1400 individuals (53.7% female) with a mean age of 58.3 (SD = 11.1) years filled in the survey. RA hip surgery was chosen by 762 (54.4%) respondents, but only 470 (33.6%) chose AI-based medical image evaluation. Those who opted for the digital technology had significantly higher educational levels and electronic health literacy (eHEALS). The majority of respondents were willing to pay to secure their preferred surgical (surgeon 67.2%, robot-assisted: 68.8%) and image assessment (radiologist: 70.9%; AI: 77.4%) methods, reporting similar average amounts in the first (p = 0.677), and a significantly higher average amount for radiologist vs. AI in the second task (p = 0.001). The regression showed a significant association between WTP and income, and in the hip surgery task, it also revealed an association with the type of intervention chosen. Conclusions: Individuals with higher education levels seem to accept the advanced digital medical technologies more. However, the greater openness for RA surgery than for AI image assessment highlights that social preferences may depend considerably on the medical situation and the type of advanced digital technology. WTP results suggest rather firm preferences in the great majority of the cases. Determinants of preferences and real-world choices of affected patients should be further investigated in future studies.

In Nigeria, statistics reveal that there is a high rate of cervical cancer among women and a significant lack of awareness surrounding Human Papillomavirus (HPV), which poses a substantial risk of HPV infection. This cross-sectional survey, conducted at Ibrahim Badamasi Babangida (IBB) University, focuses on adapting and exploring the factors that influence a 20-item scale to measure HPV knowledge, evaluating knowledge-associated patterns and HPV-associated risk factors. We examined HPV vaccination rates, infection awareness, vaccine awareness, and the impact of ethnicity on HPV knowledge. Various validated forms were adapted to measure HPV awareness and knowledge. Non-parametric tests addressed non-normality. Data were presented using median and IQR and categorical data were frequency-based. Bivariate tests (Mann–Witney, Kruskal Wallis) explored knowledge-associated factors, while quantile regression (75th percentile) examined HPV knowledge factors. Variables were considered statistically significant at p < 0.05. The adapted 20-item knowledge scale revealed strong reliability (Cronbach’s alpha = 0.913), ensuring internal consistency. The median knowledge score was 0, with an interquartile range (IQR) of 0–5. Our findings revealed a significant lack of awareness and knowledge about HPV; only 34.8% of the population were aware of HPV infection and 25.0% were familiar with HPV vaccination. Furthermore, ethnicity was found to be significantly associated with knowledge of HPV. This study emphasizes the necessity for targeted interventions to enhance HPV awareness, especially within specific ethnic groups. Despite a robust knowledge scale, educational initiatives such as seminars/conferences about HPV and cervical cancer remain crucial in addressing this gap, ultimately reducing HPV infection and cervical cancer risks in Nigeria.

Nowadays, virtual reality is a new and rapidly developing technology that provides the opportunity for a new, more immersive form of data visualization. Evaluating digitized pathological serial sections and establishing the appropriate diagnosis is one of the key task of the pathologist in the daily work. The type of tools used by pathologists in the evaluation of samples has not changed much in recent decades. On the other hand, the amount of information required to establish an accurate diagnosis has been significantly increased. Nowadays, pathologists are working with the help of multiple high-resolution desktop monitors. Instead of the large screens, the use of virtual reality can serve as an alternative solution, which provides virtualized working space for pathologists during routine sample evaluation. In our research, we defined a new immersive working environment for pathologists. In our proposed solution we visualize several type of digitized medical image data with the corresponding meta data in 3D, and we also defined virtualized functions that support the evaluation process. The main aim of this paper is to present the new possibilities provided by 3D visualization and virtual reality in digital pathology. The paper presents a new virtual reality-based examination environment, as well as software functionalities that are essential for 3D pathological tissue evaluation.

The proliferation of cyber–physical systems in modern vehicles, characterized by densely interconnected Electronic Control Units (ECUs) and heterogeneous communication networks, has significantly expanded the automotive attack surface. Traditional Threat Analysis and Risk Assessment (TARA) methodologies remain predominantly manual processes that exhibit limitations in scalability, and comprehensive threat identification. This research addresses these limitations by developing a formalized framework for automating attack path analysis within the automotive architecture. While attack graph methodologies have demonstrated efficacy in conventional information technology domains, their application within automotive cybersecurity contexts presents unique challenges stemming from domain-specific architectural constraints. We propose a novel Graph-based Attack Path Prioritization (GAPP) methodology that integrates Extended Finite State Machine (EFSM) modeling. Our implementation employs the Neo4j property graph database architecture to establish the mappings between architectural components, security states, and exploitation vectors. This research contributes a systematic approach to automotive security assessment, enhancing vulnerability identification capabilities while reducing analytical complexity.