Technological developments, especially in virtual reality (VR), augmented reality (AR), and mixed reality (MR), have created new possibilities in medical and nursing education. The metaverse, as an interactive and communal virtual environment, enables realistic clinical simulations that are free from the hazards of the real world. Considering that nursing education requires both theoretical understanding and practical skill acquisition, the metaverse may enhance clinical training, increase learner engagement, and strengthen decision-making abilities. Given the nascent nature of this topic, a comprehensive evaluation of the current research may provide significant insights into the metaverse's influence on nursing curriculum design and execution. To investigate the impact of metaverse technologies on the design, implementation, and efficacy of nursing education curricula using a comprehensive review of academic research. The use of metaverse technology in nursing education is rapidly expanding, transforming learning experiences and the development of clinical skills. Platforms such as VR, AR, and MR offer realistic simulations for procedural practice, decision-making, and real-time feedback. These instruments have shown improvements in information retention, learner engagement, clinical decision-making, and student satisfaction. Wearable gadgets and haptic feedback systems have improved practical training. Nonetheless, obstacles such as elevated prices, the need for substantial infrastructure, significant learning curves, and ethical issues like privacy and digital weariness have been documented. Metaverse technologies hold considerable promise for improving nursing education; however, their successful application relies on infrastructure preparedness, faculty training, and deliberate instructional design.

Ethical use of the human body for teaching anatomy is not transgressive.

The practice of patient optimization seems to be straightforward, logical, and not controversial, but ethical issues may arise. This most commonly occurs when the desire for risk reduction competes with concerns regarding access to care. There is no uniformity in how patient optimization is defined or achieved, and thus the outcomes of optimization programs are highly variable. The ethical tenets of beneficence, nonmaleficence, autonomy, and justice can help to guide decision making. The framework proposed helps the surgeon to consider a potentially modifiable risk factor that involves three questions: (1) What is the relationship between the risk factor and complications? (2) Is this risk factor modifiable, and to what degree can it be changed or improved? (3) If the risk factor is modified, does this actually result in reduced risk? To demonstrate application of this framework and the ethical tenets, we consider body mass index as a possible optimizable risk factor for total joint arthroplasty.

Rapid advances in large language models (LLMs) have led to the emergence of agentic artificial intelligence (AI) systems capable of autonomously performing complex scientific tasks. This review examines recent developments in agentic AI, highlighting their transformative potential for ophthalmology research and clinical practice, and discusses associated ethical considerations. Recent studies demonstrate that agentic AI systems can autonomously execute tasks traditionally performed by human researchers, including peer review, hypothesis generation, systematic reviews, and experimental design. Notable examples include AI-generated manuscripts accepted through peer review, automated systematic reviews outperforming human accuracy and efficiency, and performing complex biomedical analyses across diverse domains. Although direct ophthalmology-specific applications remain nascent, the field's data-rich nature positions it ideally for adopting agentic AI in several areas such as automated chart review, health economics modeling, and enhanced image analysis. Agentic AI represents a paradigm shift in scientific research, offering significant opportunities to enhance productivity, rigor, and innovation in ophthalmology. However, integration into clinical and research workflows necessitates careful consideration of ethical issues, including authorship attribution, data privacy, bias mitigation, and accountability. Clear governance frameworks, rigorous validation standards, and interdisciplinary training will be essential to responsibly harness agentic AI in ophthalmology.

Ethics and Organ Donation After Cardiac Arrest.

Background This study aimed to determine how obstetricians’ knowledge of T18 influences their attitudes toward the antepartum, intrapartum, and postpartum management of T18 cases and to compare these attitudes with their approaches to trisomy 21 (T21) cases. These attitudes were analysed within the framework of the medical ethics principles of beneficence and autonomy. Methods This cross-sectional descriptive survey study was conducted between 1 February and 31 July 2021. Data from 194 of 388 participants who completed all questionnaire items were analysed. Results The study included specialists in obstetrics and gynecology (mean age: 47.8 ± 8.9 years; 56.2% female). 40.2% defined “lethal congenital malformation” as anomalies inevitably causing intrauterine death; 91.2% considered T18 “lethal” and 77.6% “incompatible with life.” For T18, 82.8% always recommended termination. When pregnancy continued, most acted in line with maternal wishes; 56.8% would prefer cesarean in fetal distress and 60.5% supported perinatal palliative care. For T21, 89.0% did not define it as lethal, 94.8% did not consider it incompatible with life, and only 57.1% always recommended termination. Multivariate analysis showed older age and religious beliefs predicted lower termination recommendation for T21; only religious beliefs predicted this for T18. Female obstetricians more often provided palliative care and fetal monitoring for T21. Conclusions This study reveals significant gaps and variability in knowledge, attitudes, and management strategies for prenatally diagnosed T18 among obstetricians in Turkey. Such variability may result in some women not being offered appropriate care options during pregnancy or delivery. More practical training and ethical guidance are needed for consistent, patient-centred care in fetal anomaly management.

Background: Artificial intelligence (AI) is increasingly applied in healthcare and sustainability-related domains to improve efficiency, health outcomes, and decision-making. However, its expanding use also raises critical concerns regarding environmental impact, ethical governance, and equitable access, necessitating a comprehensive synthesis of existing evidence. Objectives: This review aims to evaluate AI applications that improve sustainability and health outcomes, compare AI-based green laboratory practices against SDG 3, identify integration challenges, compare resource optimization strategies, and analyze interdisciplinary collaboration frameworks. Methods: A systematic analysis of 50 global studies using diverse AI methodologies, including machine learning, deep learning, IoT, and green computing was conducted, with a focus on empirical and conceptual evidence from clinical and research laboratories. Results: Findings reveal that AI significantly improves laboratory efficiency, diagnostic accuracy, and resource management, achieving measurable reductions in energy, water, and waste while advancing personalized healthcare. However, the environmental costs of AI, ethical issues such as algorithmic bias and equitable access, and limited scalability in low-resource settings remain critical challenges. Multisectoral collaboration and governance frameworks are crucial but underdeveloped for the responsible deployment of AI in green laboratories. Conclusion: Integrating AI innovation with sustainability goals requires balancing technological advancements with ethical and environmental imperatives. These insights inform a theoretical framework and practical strategies for optimizing AI integration in sustainable laboratory environments, supporting global health and well-being goals within defined temporal and geographic research boundaries.

This article considers the supervision in arts therapies in an epistemic and ethical perspective as well as for its clinical implications. Historically supervision is defined as an attribute of psychiatrists, psychoanalysts, psychologists or psychotherapists. Ability to transfer/countertransfer analysis and/or pathology aknowledgements might appear as fullfilling criteria for a qualified supervision in arts therapies. But, if we believe that arts therapies involve deep understanding of the creative process in the context of a specific therapeutic relationship, then the supervisor has to be trained in the phenomenological and intuitive approaches of the art/creative praxis and of the empathic creativity. Exclusively verbal communication in supervision limits the creative resonance and empathy as it is focused on logical/analytical thinking. Using response art offers the opportunity of a deeper atunement in the client/arts therapist/supervisor’s triangle. Viewing client’s production, sharing art therapist’s response art and involving the supervisor’s own creative resonance fosters a multilayers intuitive knowledge. This is corresponding to the unconcious emotional dynamics at work in arts therapies. In this perspective, considering the use of arts-based supervision is an epistemic necessity as well as an ethical issue. It offers responses both to the epistemic paradoxe and to the ethical dilemma of verbalizing on non-verbal phenomena. The arts-based supervision is focused on the arts therapies’ core - and thus able to offer pertinent help both to the client’s clinical needs and to the supervisee’s professional growing. A few clinical examples illustrate these reflections.

ABSTRACT This paper assesses stakeholder perspectives on ethical issues in the private sector genomics industry; specifically, it examines perspectives from executives of genomics companies, using qualitative group interviews. As part of the third round of a larger Delphi study, private sector leaders were asked to provide their views on issues related to diversity (in both the genomics workforce and amongst genomics study participants), data privacy and governance laws, and benefit-sharing, in addition to the industry’s role (or lack thereof) in addressing these challenges. Our findings show 1) general consensus that industry should be a primary driver of privacy innovation amidst a difficult landscape for aligning government-based privacy regulations, 2) linking of diversity to privacy issues in the industry, and 3) a tendency to ascribe responsibility for change to actors other than those within private sector companies.

In the era of transition from digital technology to intelligence, Generative Artificial Intelligence (AIGC) is reshaping the existence and communication logic of cultural heritage. As a unique diasporic cultural space in China, Jiangmen Qiaoxiang (hometown of overseas Chinese) faces the decline of its physical field and the rupture of intergenerational memory regarding its Intangible Cultural Heritage (ICH). Traditional digital protection often stops at static "mirror replication," failing to respond to the needs of living inheritance across time, space, and cultures. Drawing on Henri Lefebvre's theory of the production of space and Pierre Bourdieu's field theory, this paper proposes the concept of "Cultural Digital Twin" and explores how Generative AI drives the ontological leap of Qiaoxiang ICH from physical space to digital twin space. The study finds that AIGC produces a digital field of virtual-real symbiosis and dynamic evolution through three mechanisms: "algorithmic reconstruction of representations of space," "embodied simulation of representational spaces," and "interactive co-creation of spatial practice." This process not only achieves the digital proliferation of ICH resources but also triggers the restructuring of power structures and the transformation of capital forms within the Qiaoxiang cultural field. The paper further constructs a digital twin space production model based on "human-machine collaboration" and critically examines issues of technological ethics, algorithmic bias, and digital sovereignty, aiming to provide theoretical interpretation and practical paths for the revival of Qiaoxiang culture in the intelligent age.

ABSTRACT The conventional approach of testing the safety and efficacy of medical devices/drugs is done through clinical trials. These trials are time-consuming, expensive and pose ethical issues. In Silico Clinical Trials (ISCTs) leverage computer simulations to assess medical devices or drugs by replicating physiological processes. Acquiring large datasets for ISCTs is challenging due to legal and ethical constraints, but synthetic data generation offers a solution. In this study, we present a widely applicable generative framework which has the capability to synthesise CT images of visceral organs and hard tissues, thereby demonstrating its extensiveness across various anatomical structures. In addition to data synthesis, we show that synthesised images are comparable to real images in terms of quality metrics and expert evaluations (data-level verification). Hence, this research shows the feasibility to use generative frameworks’ for clinically realistic data synthesis. Furthermore, we introduce a multiphasic verification strategy with an additional verification step (model-level verification) for prospective utilisation for ISCTs.

Traditional​‍​‌‍​‍‌ methods of managing student behavior in special education have typically been heavily dependent on the use of reactive strategies. Examples of these strategies include punishment, exclusion, or the use of token reinforcement systems. These techniques may temporarily suppress the expression of undesired behaviors; however, they frequently do not inculcate the required adaptive skills, nor do they encourage the development of self-regulation over time. Recently, a series of innovations in educational technology (EdTech), such as artificial intelligence (AI)–powered monitoring systems, wearable biofeedback devices, virtual reality (VR) applications, and gamified behavioral learning platforms, have been very influential in the field of behavioral intervention. This paper provides a critical evaluation of how the use of digital instruments can facilitate a shift in the disciplinary paradigm in special education from the use of punishment-based methods to the development of skills and the regulation of emotions. The current research, informed by the theoretical concepts of Positive Behavioral Interventions and Supports (PBIS), Social and Emotional Learning (SEL), and Universal Design for Learning (UDL), integrates empirical evidence from 2015 to 2025 to examine the role of technology in supporting the proactive, inclusive, and humane management of behavior. The review of literature leads to the formulation of the implementation strategies, pinpointing the ethical issues involved, and spotting the potential topics for future research, such as the challenges of integrating data-informed, empathetic, and skill-centered digital behavior interventions in the lives of individuals with data-informed digital ​‍​‌‍​‍‌interventions.

Artificial intelligence technology for the ethical issues research from a Marxist perspective under deep learning.

The elements influencing Thai university faculty members' behavioral intents to employ generative artificial intelligence in their academic work are investigated in this study. The unique features of GAI adoption were captured using an expanded UTAUT framework that included felt satisfaction and perceived risk. A bilingual questionnaire was utilized to gather information from faculty members at several Thai universities, and the suggested associations were assessed using structural equation modeling. The findings indicate that while perceived risk has a negative impact, performance expectancy, effort expectancy, perceived enjoyment, and social influence all strongly predict faculty members' inclinations to adopt GAI. Through effort expectancy, facilitating situations have an indirect impact on intention. These results show that in order to encourage responsible and successful GAI adoption, colleges must improve training opportunities, bolster institutional support, and address ethical and practical issues. The study offers empirical insights into the factors that influence GAI adoption in the setting of higher education.

This study analyzes the development of chatbot research in digital education during the 2020–2025 period using a Systematic Literature Review (SLR) approach on 100 publications, consisting of 40 national papers and 60 international papers. The results indicate a significant increase in the number of studies in 2023–2024, coinciding with the widespread adoption of Generative AI technologies such as ChatGPT, Gemini, and Bard. National studies predominantly focus on the development of AI chatbots and tutor chatbots for interactive learning, whereas international studies demonstrate greater diversity by exploring administrative chatbots, gamified chatbots, and Large Language Model based chatbots. Overall, chatbots have been shown to enhance learning motivation, the effectiveness of online learning, academic administrative efficiency, and the development of 21st-century skills. However, challenges remain, including ethical issues, response accuracy, academic integrity, and educators’ readiness to adopt these technologies. The synthesis of findings across studies further reveals that the successful implementation of chatbots is influenced by technological, pedagogical, interaction design, and social dynamics factors. Therefore, this study recommends the development of chatbots based on human-centered AI, the enhancement of digital literacy, and the expansion of research into underexplored domains to support the effective, ethical, and sustainable implementation of chatbots within the digital education ecosystem.

Autism Spectrum Disorder (ASD) presents as a complicated neurodevelopmental disorder which leads to social communication challenges and repetitive behavioral patterns. Early identification of ASD is crucial to facilitate early intervention that can make a large positive impact on long-term developmental outcomes. With the advent of artificial intelligence (AI) and data-driven diagnoses, there is increased interest in combining machine learning methods with biological and behavioral signatures to detect early ASD. This review provides an overview of broad classes of biomarkers-behavioral, neuroimaging, genetic, and eye gaze-and their respective methodologies, clinical applications, and diagnostic accuracy. For each of these biomarker domains, the research gap has been identified as existing for instance limited interpretability in neuroimaging models, genomics-related ethical and data accessibility issues, and innovation saturation for behavioral measurement. A comparative analysis highlights eye gaze analysis as a promising but under-explored option, providing a balance of cost-effectiveness, non-invasiveness, and potential for real-time, objective measurement. In addition, the application of Explainable AI (XAI) methodologies across these biomarker fields is discussed in order to meet the pressing need for transparency, clinical confidence, and decision-making support. This review makes a final call for further exploration of eye gaze-based models enriched by XAI methods as a future research direction towards filling the gap between algorithmic innovation and real-world, interpretable diagnostics in the context of ASD research.

The development of digital technology has brought significant changes to student behavior patterns, ways of thinking, and value systems, particularly at the vocational secondary education level. While digital technology opens up significant opportunities for improving the quality of learning, it also raises various ethical issues. This situation demands the integration of digital ethics into the educational process, including in Islamic Religious Education (PAI), which has a strategic mandate in shaping students' character and morals. This study aims to describe how the integration of digital ethics is implemented in Islamic Religious Education (PAI) learning at Vocational High School 4, Serang City (SMKN 4 Serang), as well as to identify the roles of teachers, curriculum, and school culture in supporting the internalization of Islamic-based digital ethical values. This study uses a qualitative descriptive approach with data collection techniques through observation, interviews, and documentation studies. Research informants included two Islamic Religious Education (PAI) teachers, the vice principal for curriculum, and 24 students. The results show that the integration of digital ethics in PAI is carried out through a process of transinformation in the form of strengthening Islamic moral values, then value transactions in the contextualization of teaching materials with digital phenomena, teacher role models, and transinternalization in the form of habituation of ethical digital behavior in the school environment. These findings emphasize the strategic role of PAI in shaping students' digital ethical awareness, despite still being hampered by teachers' digital competence and suboptimal school policies.

Introduction. Breast cancer is one of the most common cancers among women worldwide. A hereditary component accounts for about 5–10% of all breast cancers, emphasizing the importance of genetic testing to detect high risk individuals. However, the process of genetic testing is associated with various psychological and ethical issues, including psychological burden on patients, the risk of genetic discrimination, and the need to inform relatives about the test results. Purpose. To evaluate the significance of pre-test counseling in the process of genetic testing for hereditary forms of breast cancer. Materials and methods . A questionnaire was created to assess the level of patient awareness about hereditary breast cancer. The impact of medical genetic counseling on sharing information with relatives was also studied. The study was conducted at the Center for Personalized Medicine of the A.S. Loginov Moscow Scientific Center. Results . It was found that patients who underwent pre-test counseling were significantly more likely to inform their relatives about the GT results. In the group without pre-test counseling, fear of receiving negative information and anxiety about data confidentiality predominated.

Human resource management has been greatly affected by the fast-paced digital change, especially in the age of remote work. The COVID-19 epidemic hastened the worldwide trend towards remote work, which in turn has altered company management. HR management needs to change to accommodate the emerging norm of remote and hybrid work as more and more businesses use these arrangements. This paper will explore the organisations can improve their HR management techniques for managing remote workers, leading virtual teams, and ensuring efficiency in digital workplaces. It is suggested that methods for acquiring talent, training, evaluating performance, and engaging employees be rethought. In order to help HR leaders and practitioners optimise remote work arrangements and successfully navigate issues, this report analyses real-world case studies & expert insights. For companies looking to strengthen their resilience and entice top people, remote employment offers obvious benefits. Human resources professionals can thrive in remote work environments if they are both proficient in technology and eager to learn. In navigating the challenges of remote work, which impacts the prospects of work and how enterprises foster a successful worker, the HR function plays a critical role. HR professionals can use the study's helpful findings to situate their organisations for success in this changing landscape.

This study examined middle school students’ perspectives on generative artificial intelligence (GenAI) tools during a week-long civic engagement camp in which students enhanced their digital media literacy and GenAI knowledge while using an action civics cycle of inquiry to investigate and propose solutions to community issues. Following a lesson that included information on using GenAI for research, students worked collaboratively on inquiry projects addressing issues such as homelessness and animal welfare. The researchers explored students’ perceptions of GenAI’s benefits and challenges using surveys, focus groups, and written responses. Findings indicated that while students appreciated GenAI’s ability to compile and provide detailed information, they expressed skepticism regarding its accuracy and trustworthiness, as well as concerns related to dependence on the technology and ethical issues. This study offers insights for educators seeking to integrate GenAI in middle school education, emphasizing the need for instructional support that addresses both the technological affordances and limitations of GenAI.