Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.

The metaverse presents several unique challenges from security and privacy standpoints. This paper offers a detailed description and analysis of technological environment and institutional factors that can lead to increased privacy violations and security breaches in the metaverse. On the technological front, it discusses how various features of the metaverse such as newness, novelty and complexity of technologies involved, data intensiveness and weak architectural security of the metaverse are likely to provide a fruitful environment for cybercriminals and other perpetrators. The paper analyzes the level and nature of the impacts of privacy violations and security breaches on consumers and victims in the metaverse environment. It also demonstrates how security breaches in the metaverse are likely to lead to immediate harms to victims. It promotes an understanding of how institutions at various level are related to privacy and security risks in the metaverse. On the regulatory front, it points out that privacy and security laws of the Web2 era are not sufficient to deal with the environments of the metaverse. It also argues that the preparedness to provide security and privacy in the metaverse’s multidimensional and multi-sensory environment is currently lacking at the industry level. • The metaverse presents several unique challenges from security and privacy standpoints. • Key features of the metaverse provide a fruitful environment for perpetrators. • Security breaches in the metaverse are likely to lead to immediate harms to victims. • Institutions at various level to handle privacy and security risks in the metaverse are underdeveloped.

Despite ongoing advancements in prevention and treatment strategies, socioeconomic disparities in infectious diseases remain widespread across the United States and continue to disproportionately affect vulnerable communities. The COVID-19 pandemic brought attention to these issues of place-specific structural predisposition related to poverty, density of housing, inadequate medical care, and digital divides, which determine both disease exposure and mortality. The present narrative literature review aggregates knowledge from geospatial analytics (GA) solutions aimed at understanding and remediating these issues. Drawing from spatial epidemiology, geographic information systems (GIS), geospatial statistics techniques, ML-GIS hybrids, and GA optimization models, this review highlights how geospatial tools can be utilized to define infectious disease burden clustering in geographically stratified areas and to visualize disease burden gradients from rural to urban areas. Infectious disease burden data, coupled with advancements from remote sensing imagery and high-resolution ML models, have led to distinct increases in accuracy to accurately target infectious disease burden areas related to vaccination and rural preparedness planning; these areas can be utilized to prevent unacceptable infectious disease burden risks for anyone. Despite these advancements and ongoing research efforts, issues persist regarding data deserts, privacy issues related to geoprofiling techniques, heterogeneity in geospatial data models, and techniques related to infectious disease burden research study experiments in rural public health organizations across areas related to geospatial data storage requirements. A geospatial analytics solution inclusive and oriented towards equity can be transformative in enabling precision public health approaches directed towards disrupting infectious disease burden inequity rather than perpetuating issues of inequity. Keywords: Geospatial Analytics, Infectious Disease Disparities, Social Determinants of Health

To assess the clinical effectiveness, safety, technical feasibility, and challenges of remote robotic surgery in general surgery, gynecology, orthopedics, and urology, focusing on issues like communication technology, ethics, and cost. Relevant reports on remote robotic surgery were retrieved from databases such as PubMed, EMbase, Web of Science, Cochrane Library, VIP, CNKI, and Wanfang (2001-2025). Literature quality was evaluated using tools from the Joanna Briggs Institute (JBI). A total of 26 articles were included (13 in Chinese, 13 in English) covering general surgery, gynecology, orthopedics, urology, and thyroid surgery. The results showed that the average operation time was slightly longer than that of traditional methods (about 10-30min), but the safety was good. Most studies had communications backup plans, such as backup networks or local physician take-overs, to deal with the risk of disruption. Tele-robotic surgery has a high success rate and accuracy with controllable network latency, showing the potential to expand access to medical resources, especially in remote areas. However, it still faces challenges such as network stability, equipment cost, operation accuracy, data privacy, and ethical issues. Most of the included studies reported successful cases, and there was a lack of in-depth analysis of failure cases or adverse outcomes of patients, which may have publication bias. Despite challenges, remote robotic surgery shows promise in overcoming geographical and resource limitations. The quality of current evidence is low, with serious methodological limitations and reporting bias. The establishment of international mandatory registration systems, standardized safety protocols, and transnational collaboration networks are urgent to promote this field from "proof of concept" to "clinical practice". But, with continuous technological advancements, it is expected to play an increasingly significant role in global healthcare.

Purpose Although study has shown that electronic performance monitoring (EPM) may have a positive impact on employee performance, it has also been suggested that it may lead to employee stress and dissatisfaction, which may inhibit performance. This study aims to provide a balanced perspective on this conflicting issue by using social information theory as an overarching framework. In addition, SIP theory was further refined by integrating attribution theory to reveal the conditions under which monitoring is most effective. Design/methodology/approach First, the authors conducted a series of confirmatory factor analyses (CFAs) using Mplus 8.3 software to assess the measurement validity of the model. A path model was then developed using maximum likelihood to test all hypotheses. Specifically, EPM was used as the independent variable, control attribution and feedback attribution as moderators, employees’ work goal progress and perceived privacy violation as mediators, and employees’ task performance as the outcome variable. Demographic variables (gender, age, education, tenure in current organization), LMX, positive affect and negative affect were entered into the model as control variables. Parametric bootstrap was used to test the mediator and the moderated mediator (5,000 repetitions, forming a 95% confidence interval) and construct the full path model. Findings The authors propose that EPM improves task performance by stimulating employees’ perception of work goal progress. Correspondingly, EPM will also stimulate employees’ perception of privacy violation, which will have an adverse impact on task performance. In addition, the authors consider employees’ different attributions of organizational implementation of EPM as moderators in the model and propose that feedback attributions strengthen the positive path of EPM-work goal progress-task performance and weaken the negative path of EPM-perceived privacy violation-task performance, while control attributions strengthen the negative path and weaken the positive path. The results supported most of the authors’ hypotheses. Research limitations/implications First, all variables were self-reported, which may lead to common method bias. However, some research suggests that self-reporting is not only an appropriate method for exploring issues within the realm of personal experience, but in some situations it is even superior to the evaluation of others. Nevertheless, the authors encourage future research to adopt multi-source data. Second, despite the use of a time-lagged design, causality could not be established. Therefore, future research is encouraged to use experiments to manipulate EPM and attributions to establish causal relationships between the variables. Third, the study was conducted in one country. In the future, this study can be replicated in other countries to solve relatively limited universal problems. Practical implications First, the research shows that EPM practice can effectively improve employees’ task performance, and the implementation of EPM is of great significance to both individual employees and organizations. However, although these technologies have significant advantages in improving work efficiency and optimizing performance management, the authors must also be wary of their potential adverse effects. Therefore, when introducing these advanced technologies, companies should carefully evaluate their potential negative effects to ensure that the application of technology will not have a negative impact on the well-being of employees. Second, the findings reveal that EPM practices do not always achieve the expected results. Therefore, if the company’s goal is to promote employees to make feedback attributions, it should formulate reasonable monitoring policies, explain the purpose of monitoring, and make monitoring more transparent to protect employee privacy and reduce the negative impact caused by privacy violation perception. Social implications With the continuous advancement of technology, EPM technology is also developing continuously, and more and more advanced technologies are being applied to employees’ performance management. For example, artificial intelligence and big data analysis technologies enable companies to monitor employees’ work performance in real time, generate detailed performance reports and provide personalized feedback. The study helps provide a theoretical basis for companies to balance efficiency and employee welfare, optimize management strategies and enhance the fairness of the work environment and employee satisfaction when implementing EPM. Originality/value First, innovation in theoretical perspective: social information processing (SIP) theory is systematically introduced into the field of e-performance monitoring research for the first time, providing a more balanced perspective on the contradictory views of EPM on employee performance. Second, research paradigm innovation: expanding the application scenarios and explanatory effectiveness of SIP theory. Most of the previous studies on SIP have focused on the effects and influence of leaders as information sources on employee behavior, while there is still a theoretical gap in the information transfer mechanism of human resource management practices. This study extends contextual cueing research from leadership behavior to HRTS by introducing SIP theory. Third, theoretical integration innovation: coupling SIP theory and attribution theory to build a comprehensive analysis model.

The digitalization of Indonesia’s judiciary has generated new ethical challenges for the legal profession, particularly in relation to access and use of electronic evidence. Key concerns include potential breaches of data confidentiality, manipulation of digital evidence, and the absence of clear ethical standards governing advocates in e-court proceedings. Current procedural law and the advocates’ code of ethics do not adequately address the complexities of electronic evidence, which inevitably raise issues of privacy, cybersecurity, and procedural justice. This study aims to examine the ethical dilemmas faced by lawyers in digital litigation in Indonesia and to formulate both normative and institutional solutions to strengthen professional accountability. Employing normative legal research in statutory, conceptual, and philosophical approaches, supported by case analysis involving evidence, the study highlights a regulatory gap in the advocates’ code of ethics and deficiencies in ethical oversight mechanisms. Missed leading in legal practices, especially in the electronic court, where everyone can access electronic evidence to support the need for regulation to protect electronic evidence in the electronic court. The findings suggest the need for reform through the incorporation of specific standards on electronic evidence within the code of ethics, enhancement of lawyers’ digital literacy, and establishment of technical guidelines by professional organizations and the Supreme Court.

Press freedom and the right to privacy are twin pillars of democratic governance and human dignity. In Nigeria, both rights enjoy constitutional protection under the 1999 Constitution (as amended), yet their coexistence has remained contentious. Media investigations, crime reporting, political exposés, and the proliferation of digital and social media have intensified clashes between the public’s right to know and individuals’ claims to privacy. This paper undertakes a doctrinal and analytical examination of the tension between press freedom and individual privacy rights in Nigeria. It analyses the constitutional and statutory framework, judicial attitudes through Nigerian case law, and relevant international and regional human rights instruments. Using the social responsibility and rights-balancing theories, the paper demonstrates that neither press freedom nor privacy is absolute and that Nigerian courts have increasingly adopted a balancing approach anchored in public interest and proportionality. The paper identifies persistent challenges such as weak enforcement, sensational journalism, and technological disruption, and proposes reforms aimed at achieving a principled and predictable balance between media freedom and the protection of individual dignity. It concludes that a responsible press and a coherent legal framework are essential for sustaining democracy and human rights in Nigeria.

Abstract Music in the current digital era is consumed primarily on-demand, for example by streaming from Spotify or YouTube, rather than by broadcasting, for example, by listening to a radio station. A strong correlation has been found between music and emotions. Hence, with our ability to select our own music and the availability of artificial intelligence (AI) tools, the importance of music emotion recognition (MER) is significant. This research proposes a novel methodology for predicting emotions from a musical piece that is selected by the listener based on a two-layer AI. The dataset and the process are exceptional in two ways: first, by their qualitative character, which minimized the inherent subjectivity of self-reported emotions, and also the high reliability of the collected data in comparison to other methods such as a questionnaire; and second, by their quantitative character, given $$\:n=\text{9,090}$$ songs from about 4,447 people. The model succeeded in predicting the primary emotion with $$\:accuracy=57\%$$ and one of the two leading emotions (the primary and the secondary) with $$\:accuracy=75.1\%$$ , and without accommodating demographic factors in the model. We believe this knowledge may be a useful tool for therapists, coaches, teachers, etc. and may also raise public awareness of a potential privacy violation.

This is a thorough literature review analysis of the moral dilemmas surrounding sociovirtualization and striking a balance between social interaction and privacy in virtual environments. By providing digital identities, virtual societies, and virtual experiences, sociovirtual sites alter social relationships and raise difficult moral dilemmas. While social contact issues focus on authenticity, access, emotional well-being, and digital divisions, privacy issues involve data collection, informed permission, anonymity, and unequal powers. Case studies, ethical theories, and governance models are used to investigate conflicts between the preservation of privacy and the promotion of authentic social connection. Important conclusions include the interdependence of privacy and social connection, the necessity of transparency, user-centric design, and regulatory involvement. The article's conclusion highlights the need of interdisciplinary cooperation in creating sociovirtual environments that uphold individual rights and encourage inclusive, sincere social interaction. Future research should focus on developing policies, bridging digital divides, and emerging technology.

The educational landscape has transformed as a result of social media integration with the system. Social media has become an essential part of our lives the way we interact with one other. E-learning is the transmission of knowledge and education using computer networks, particularly the internet. The objective of this research article is to study the strengths, weaknesses, opportunities and challenges of social media in e-learning. The strengths of social media in e-learning are better interaction, immediate data access, better potential for growth, quick and easy access, increased involvement of students, tools for content creation, free educational opportunities. On the other hands, the weaknesses are distraction issues, privacy issues, authenticity of data, availability of unsuitable content, adverse effect on mental health. Beside this, the opportunities of social media in e-learning are as micro-learning, social and interactive experiences, gamification, Combining Virtual Reality (VR) and Augmented Reality (AR), content created by users, AI-assisted education. There are various challenges are as data security and privacy issues, sustaining levels of student involvement, managing social and academic links, assessing the appropriateness of content, linking various social media sites, bringing inclusivity and accessibility into consideration, controlling social media time, consistency of content on all platforms, distractions and effectiveness, overcoming technological constraints. So, this paper explores the strengths, weaknesses, opportunities and challenges of social media in e-learning.

Purpose This study examines the dual impact of artificial intelligence (AI) on business processes by systematically identifying and prioritizing both its value-creating benefits (bright-side) and risk-inducing consequences (dark-side). The study further aims to develop an integrated theoretical framework that explains how organizations can balance these opposing effects to achieve sustainable AI-driven business transformation. Design/methodology/approach A content-driven review of recent peer-reviewed literature was conducted to identify key positive and negative impacts of AI across five business process dimensions. Expert evaluations from 42 professionals were analyzed using the Fuzzy Technique for Order Preference by Similarity to Ideal Solution method to prioritize these impacts under uncertainty. Sensitivity analysis was performed to validate the robustness of the results, and an integrated theoretical framework was developed based on the empirical findings. Findings The results reveal that AI delivers its strongest benefits through enhanced financial risk forecasting, internal control, sustainability reporting and customer-facing automation, significantly improving operational efficiency and decision quality. However, the findings also highlight critical risks associated with inadequate governance, privacy invasion, ethical lapses and excessive reliance on automated systems. Governance-related risks emerged as the most severe challenges, indicating that the absence of robust ethical and regulatory frameworks can undermine the long-term value of AI adoption. Practical implications The study provides managers and policymakers with a structured decision-support approach to prioritize AI applications while proactively managing ethical, governance and data-related risks. Originality/value This research offers a novel integration of fuzzy multi-criteria decision analysis with theory development to present a balanced, data-driven understanding of AI's dual impact on business processes and organizational sustainability.

Virtually delivered psychotherapy is a widely adopted means of providing mental health interventions. There is little research on patients' perspectives on this mode of delivering therapy, and their perspectives are important given that patient factors predict a large proportion of mental health outcomes. This study explores patients' experiences with online psychotherapy to help inform best practices. Thirty-three patients who were currently receiving synchronous, virtually delivered psychotherapy were interviewed. The interview focused on patient experiences, both positive and negative, and their recommendations for improving virtually delivered therapy. Interviews were audio-recorded and transcribed. We used an inductive approach to thematic analysis in coding the transcripts. Audit trails and reflexive and independent coding ensured the trustworthiness and fidelity of the identified themes. Themes reflecting positive patient experiences included convenience, accessibility, and connection with the therapist. Themes of negative experiences included technology-related challenges, perceived disconnection due to not being physically present with a therapist, concerns about safety, and privacy issues. Patient characteristics and personal circumstances interacted with the online modality to affect their experiences. Patients recommended enhancing privacy and safety, utilizing online tools effectively, and encouraging therapists to be more deliberate in their non-verbal communication. For clinicians and researchers, this study outlines patients' perspectives on the aspects of virtual care that work effectively and those that could benefit from further improvement.

This study examines the transformation of the political economy of communication in Nigeria through the lens of digital platforms. As traditional media outlets migrate to digital spaces, new revenue streams and intermediaries have emerged, creating a 24-hour news cycle governed by algorithms and the blogosphere. By reviewing existing literature, this paper analyses how digital platform mechanisms, specifically algorithms, advertising, and commercialisation, shape voter decisions and political discourse. The study identifies a significant surge in digital platform activity, ranging from political contests to the spread of disinformation, which often prioritises platform profitability over public interest. Key themes include digital literacy, ownership concentration, and the sustainability of the Nigerian media market. The findings suggest that while these platforms enhance civic engagement, they also facilitate economic misinformation and privacy infringements. The paper concludes with recommendations for regulatory oversight to counter misinformation and protect the Nigerian market from digital oversaturation.

The rapid growth of digital technology has transformed the way individuals access education, employment, healthcare, governance, and information. In contemporary India, internet connectivity is no longer a luxury but an essential requirement for meaningful participation in social and economic life. This paper examines whether the Right to Digital Access can be recognized as an integral part of Article 21 of the Constitution of India, which guarantees the Right to Life and Personal Liberty. The background of the study lies in the judicial expansion of Article 21, where courts have interpreted “life” to include dignity, privacy, livelihood, and other essential rights necessary for a meaningful existence. The central research problem addressed in this paper is whether denial or unreasonable restriction of digital access affects the constitutional guarantee of life and liberty. The study also explores the connection between digital access and other fundamental rights, including freedom of speech and expression, equality, education, and privacy. The research adopts a doctrinal and analytical methodology. It relies on constitutional provisions, landmark judicial decisions, scholarly writings, and government policies related to digital infrastructure. Key judgments concerning privacy and internet restrictions have been examined to understand evolving judicial trends. The findings indicate that digital access is closely linked to the realization of multiple fundamental rights. Although it has not yet been expressly declared as an independent fundamental right, judicial reasoning suggests that access to the internet forms an important part of constitutional freedoms in the digital age. The paper concludes that recognizing digital access within the framework of Article 21 would strengthen democratic participation, promote equality, and ensure that technological progress benefits all sections of society. Keywords-Article 21, Right to Digital Access, Digital Divide, Internet Shutdown, Fundamental Rights

Female sex workers (FSWs) in sub-Saharan Africa are at high risk of HIV acquisition. Here we explore the values and preferences of Zimbabwean FSW for long-acting pre-exposure prophylaxis (PrEP). We employed mixed methods; focus group discussions (FGD) (n=15), a respondent-driven sampling (RDS) survey (n=4444) from 22 sites across Zimbabwe and a nested discrete choice experiment (DCE) (n=435) conducted in 4/22 sites in 2021. Purposively selected FSWs aged 18 or over who reported being HIV negative were eligible for inclusion in FGDs. Analysis of self-reported HIV negative survey participants was RDS-II weighted. DCE analysis estimated relative preferences. Qualitative and quantitative data were triangulated. Median age of survey participants was 28 years with IQR of 23-34 years. There was strong concordance across methods by product, provider, service and individual characteristics. Most FSWs indicated that they preferred long-acting injectable (LAI) PrEP to either oral PrEP or dapivirine vaginal ring (DVR). Most were interested in using LAI PrEP (74.1%; n=1835/2392), a few the DVR (10.9%, n=230/2392), and 2.4% (59/2392) and 13.5% (268/2392) either or neither of the two options, respectively. There was little trust in public sector healthcare providers, with most FSWs opting to access PrEP through programmes designed for sex workers (and stating they would miss a prescription refill/repeat injection if the public sector was the only available option). Injectable PrEP addressed privacy and adherence concerns to some extent, although FSWs felt that 6-monthly would be preferable to less frequent injections. Issues of privacy (related to PrEP and being a FSW), confidentiality and respect emerged as key qualitative themes. FSW had a strong preference for LAI PrEP, but ensuring product choice and user privacy was key. FSW in Eastern and Southern Africa should therefore be prioritised for PrEP choices, with ongoing monitoring and evaluation of services to make sure they are acceptable, effective and evolve as products and delivery options become available.

Digital tools and generative artificial intelligence are becoming more common as part of student artresearch, for source searches, visualidea prototyping, text drafting and creative inquiry documentation. At the same time, fast production can also hide weak verification practices,as well as pervious authorship limits and frail methodological traceability. This paper investigates how a well-organized digital research toolkit and explicit governance rules impactmethodological quality, risk levels and pedagogical effectiveness in practice-based, including practice-led student research in visual arts and art education.The study will employ a mixed-method andquasi-experimental design of two equivalent groups. Quantitative results are assessed using an analytical rubric targeting research traceability (quality of sources and accuracy of citation, transparency in methodology, alignment between evidence and interpretation, originality with reflectivecritique analysis and digital outputs integrity) together with scenario-based generative artificial intelligence literacy testing as well as student self-report measures. Qualitative evidence is garnered throughportfolio and process-log analysis, interviews, focus groups and critical-incident reporting to account for mechanisms underpinning change events.Our results suggest that the toolkit-plus-governance strategy delivers significant enhancements in research traceabilityand rigor, particularly improvements in citation accuracy, documentation completeness, and critical assessment of generative outputs. Risk factors—such as non-evidenced references, misattribution and privacy violations—are reduced when norms of disclosure,timelylogs, or verification activities are embedded within teaching and assessment. This discussion contextualizes these findings within current international recommended practices around ethical use of artificial intelligence, and implications for rubric-based assessment re-design, educator competencies, and policy-relevant risk management inArt education.The article suggests that the pedagogical potential of digital tools is less about the novelty of tools and more about the alignment between disciplinary goals, assessment criteria, and governance measures thatrender student research transparent, auditable and ethically defensible.

AI conversational agents in older adults with chronic disease: A scoping review.

Optimal decision-making and complex analysis in green supply chains considering blockchain and consumer privacy concern

Human activity recognition (HAR) is essential in many applications, such as smart homes, assisted living, healthcare monitoring, rehabilitation, physiotherapy, and geriatric care. Conventional methods of HAR use wearable sensors, e.g., acceleration sensors and gyroscopes. However, they are limited by issues such as sensitivity to position, user inconvenience, and potential health risks with long-term use. Optical camera systems that are vision-based provide an alternative that is not intrusive; however, they are susceptible to variations in lighting, intrusions, and privacy issues. The paper uses an optical method of recognizing human domestic activities based on pose estimation and deep learning ensemble models. The skeletal keypoint features proposed in the current methodology are extracted from video data using PoseNet to generate a privacy-preserving representation that captures key motion dynamics without being sensitive to changes in appearance. A total of 30 subjects (15 male and 15 female) were sampled across 2734 activity samples, including nine daily domestic activities. There were six deep learning architectures, namely, the Transformer (Transformer), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D CNN), and a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture. The results on the hold-out test set show that the CNN-LSTM architecture achieves an accuracy of 98.78% within our experimental setting. Leave-One-Subject-Out cross-validation further confirms robust generalization across unseen individuals, with CNN-LSTM achieving a mean accuracy of 97.21% ± 1.84% across 30 subjects. The results demonstrate that vision-based pose estimation with deep learning is a useful, precise, and non-intrusive approach to HAR in smart healthcare and home automation systems.

Breast Cancer (BC) is the most diagnosed and the second most frequent cause of death among women. Despite the wide application of mammography as a screening technique, the low sensitivity of this technique in dense breasts, and the risks of radiation are significant disadvantages. In diagnosing and early treatment planning of breast cancer, medical imaging is very important. This paper provides a framework for the segmentation and 3D reconstruction of breast tumors based on a 3D NIFTI image dataset. The preprocessing step entails using an integration of Non-Local Means (NLM) filter and Sobel edge detector to effectively minimize noise and maintain significant structure of the breast tissue. Among the three deep learning models, including UNet, ResNet, and a hybrid architecture of UNet-ResNet, the segmentation of tumor regions is carried out. These models are tested on traditional measures such as Dice Coefficient and Jaccard Index, with the hybrid UNet-ResNet model performing better than others, with a Dice coefficient of 1.38 and a Jaccard index of 0.08. After the segmentation, the volume of the tumor is estimated through the spatial dimensions of the tumor, and the Marching Cubes algorithm is used to create a 3D image of the cancerous tissue to be easily visualized. To prevent the invasion of patient privacy and data security, the system will involve encryption-based storage, anonymization, and secure access control, which are in line with healthcare data protection. This framework not only enhances the accuracy of tumor segmentation but also ensures safe handling of sensitive MRI data, which eventually helps in making better clinical decisions.