Purpose Artificial intelligence (AI)-assisted recruitment is evolving in the hospitality industry. However, a critical gap persists in understanding what recruiters expect from AI tools and how to design them to meet those expectations. To address this gap, this study aims to adopt a dual-perspective approach, examining the drivers and outcomes of AI-assisted recruitment logistics from the viewpoints of recruiters and recruits. Design/methodology/approach The authors used a mixed-method approach across two studies. Study 1 consisted of 12 interviews with hospitality managers to explore their desired outcomes of using AI tools in the recruitment process. Study 2 involved the development of a chatbot-based scheduling system, followed by a survey with 256 participants to examine what drives the desired outcomes from the candidates’ perspective. Findings Study 1 reveals that beyond operational efficiency, recruiters use AI tools to enhance candidate satisfaction with the recruitment experience and to improve recruiter trustworthiness and attractiveness. Study 2 demonstrates that the key drivers of these outcomes are perceived usefulness, trust and enjoyment. Practical implications AI tools that assist the recruitment process should be designed to enhance candidate satisfaction and positively influence perceptions of recruiter credibility. To achieve this, their design should prioritize perceived usefulness, address privacy and security concerns and offer an enjoyable user experience. Originality/value This study investigates real user interactions with a functioning scheduling chatbot, offering a more authentic and valid assessment. Moreover, it extends the application of AI tools in hospitality beyond customer service, shedding light on their role in recruitment logistics.

With the widespread use of mobile devices, mobile payments have become increasingly popular worldwide. In China, third-party providers such as Alipay and WeChat Pay dominate the mobile payment landscape. However, drawbacks of these systems have gradually surfaced, prompting the development of Central Bank Digital Currency (CBDC) payments, most notably China’s Digital Yuan (e-CNY). Despite its advantages, CBDC adoption remains limited compared with established third-party platforms. This study investigates the factors influencing users’ switching from third-party mobile payments to CBDC payments. Unlike third-party systems, CBDC belongs to the cash category (M0), making it distinct in nature and warranting a tailored research framework. To capture this complexity, the Push-Pull-Mooring (PPM) model was employed, integrated with privacy calculus theory, status quo bias theory, and national identity theory, to construct a comprehensive model of switching intention. Methodologically, a combined approach of partial least squares (PLS), artificial neural networks (ANN), and fuzzy-set qualitative comparative analysis (fsQCA) was adopted. This hybrid strategy enhances robustness and provides nuanced insights into the interplay of causal factors. The findings reveal that privacy concern and trust act as key drivers of users’ intention to switch to CBDC payments, whereas inertia represents a significant barrier. These results enrich theoretical research on CBDC payments and extend the application of the PPM model in a novel financial context. Practically, the study informs policymakers and managers by highlighting the importance of targeted strategies that strengthen trust, address privacy issues, and reduce switching barriers, thereby accelerating CBDC adoption.

This study examines how organisational and individual factors influence Knowledge Management Outcomes (KMO) through knowledge collaboration and behaviours enabled by the Metaverse. Grounded in Knowledge Management Theory (KMT) and supported by related theories, the study proposes a structural model that includes Organisational Readiness (OR), User Trust (UT), Privacy Risk (PR) and Digital Literacy (DL). The model explores how these factors affect Metaverse-Enabled Knowledge Collaboration and behaviours, and their impact on KMO. Using survey data from six advanced manufacturing industries, the study validates the model through Partial Least Squares Structural Equation Modelling. The results show that DL significantly moderates knowledge behaviours. Multi-group analysis reveals that in the High-End Equipment Manufacturing industry, OR has a stronger effect on knowledge collaboration, PR also has a stronger negative impact on knowledge application and DL more effectively moderates the relationship between knowledge collaboration and knowledge sharing. This study extends KMT by providing an analytical framework for complex knowledge management processes in a Metaverse-enabled environment. It highlights the role of Metaverse Technology in reshaping knowledge flows. Practically, the study emphasises the importance of OR and DL. Enterprises should build trust in emerging technologies, address privacy concerns and tailor deployment strategies to industry-specific needs to optimise KMO.

BackgroundElectronic personal health records (e-PHRs) can improve health management; however, people with disabilities face adoption barriers. Identifying acceptance drivers in this population is essential.ObjectiveThis study aims to determine factors shaping intention to use e-PHRs among people with disabilities within a technology acceptance model (TAM) framework, including external determinants (health consciousness [HC], health information consent [HIC], content characteristics [CC], information security [IS], eHealth literacy [eHL], and effectiveness [EF]).MethodsA nationwide survey of people with disabilities in South Korea (N=800) was conducted across rehabilitation hospitals, disability welfare centers, and public health centers (August 30 to November 30, 2023) using proportionate stratified and systematic stratified cluster sampling. Hypotheses were tested via structural equation modeling with bootstrapped mediation (2000 resamples) and multigroup analyses by disability severity.ResultsUsage intention (UI) was primarily driven by perceived usefulness (PU; β=0.662; P<.001) and additionally by perceived ease of use (PEU; β=0.203; P<.001). Ease of use increased usefulness (β=0.452; P<.001). External predictors of PEU were HC (β=0.233; P<.001), CC (β=0.163; P<.001), HIC (β=0.167; P<.001), IS (β=0.089; P=.005), and EF (β=0.276; P<.001); eHL was not significant (β=0.025; P=.41). Predictors of PU were EF (β=0.368; P<.001) and HIC (β=0.243; P<.001), while CC (β= −0.121; P=.002) and eHL (β= −.068; P=.003) were negative; HC and IS were not significant. Indirect effects supported PEU→PU→UI (β_indirect=0.299; 95% CI 0.210‐0.404). The largest total upstream effects on associations with intention were EF (β_total=0.382; P<.001) and HIC (β_total=0.245; P<.001). Multigroup structural equation modeling (mild, n=432; severe, n=368) indicated PU was a stronger driver of intention in the mild group (β=0.727) than the severe group (β=.511). PEU also contributed (severe β=0.272; mild β=0.171). CC predicted PEU only in the mild group (β=0.201; P<.001), whereas IS predicted PEU only in the severe group (β=0.119; P=.003).ConclusionsThis study highlights that PU and PEU are crucial mediators driving the adoption of e-PHR among people with disabilities. These findings suggest the need for designing user-friendly digital health solutions that integrate robust support systems, address privacy concerns, and deliver high-quality, relevant content tailored to this population. The restriction to people with disabilities using rehabilitation, public health, or welfare centers introduces selection bias. Future studies should broaden sampling to include a diverse population.

Purpose The study explores the acceptance of AI-driven virtual teaching assistants (VTAs) in Vietnam’s online learning. It aims to identify factors influencing students’ intention and actual use of these emerging technologies. Design/methodology/approach Using an extended Unified Theory of Acceptance and Use of Technology (UTAUT2), the research adds two constructs – privacy concerns and learning value (LV) – and includes robotics adoption as a moderating factor. A survey of 500 university students in Ho Chi Minh City, who use or plan to use VTAs, was analyzed through structural equation modeling to test the model’s predictive power. Findings The results show that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit, LV and privacy concerns significantly shape behavioral intention to adopt VTAs, which in turn predicts actual usage. Students are more likely to embrace VTAs when they perceive them as useful, easy, enjoyable and beneficial, provided privacy risks are mitigated. Robotics adoption further strengthens the link between intention and use. Practical implications The study suggests that educational institutions should highlight VTAs’ learning benefits, ensure usability, address privacy issues and invest in supportive infrastructure to boost adoption. Originality/value This research is among the first to study AI-based VTAs in Vietnamese higher education using an extended UTAUT2 model. By replacing “price value” with “learning value” and stressing privacy, it enriches understanding of artificial intelligence adoption in education and offers insights applicable beyond Vietnam.

The integration of digital tools in aesthetic medicine has enhanced the precision of facial feature analysis. Using concepts like the Golden Ratio, these technologies enable more objective assessments of facial proportions and symmetry. The beauty scanner-face analyzer (BS-FA) app offers a digital approach to evaluate geometric proportions and facial alignment, providing valuable data for preoperative planning in plastic surgery and aesthetic treatments. This study evaluates the effectiveness of the beauty scanner-face analyzer (BS-FA) app in assessing geometric facial proportions based on the Golden Ratio and analyze facial alignment for symmetry-key factors in plastic surgery and aesthetic treatments. A dataset of 14 front-facing images from 7 females and 7 males was processed using the BS-FA app. The app analyzes various facial zones by dividing the face into horizontal and vertical sections and measures the proportions of features like the eyes, nose, and mouth relative to idealized aesthetic standards. The app's output includes a beauty score, geometric proportions, and symmetry assessment. Additionally, three evaluators independently tested the same images multiple times to assess the consistency of the measurement values provided by the app. Ideal proportional ranges were established by senior plastic surgeons, serving as a useful reference for both plastic surgeons and app users, and providing guidance for interpreting facial measurements and identifying potential discrepancies in facial proportions. The BS-FA app successfully provided detailed measurements of facial proportions and symmetry across 14 models (7 females and 7 males). The proportion analysis, based on the Golden Ratio, revealed varying degrees of alignment with ideal facial proportions, particularly in features like the nose, eyes, and mouth. The values for all 14 images remained consistent after being tested multiple times by the three evaluators using the same set of images. The symmetry assessment showed high levels of balance, with symmetry percentages ranging from 93 to 99% across different facial features. While most faces exhibited near-perfect symmetry, slight asymmetries were noted in features such as the eyebrows and mouth, indicating potential areas for aesthetic refinement. The BS-FA app offers valuable insights into facial symmetry and proportions, aiding preoperative planning in plastic surgery. While it supports balanced and natural results, its limited sample size and the subjective nature of beauty mean it should complement, not replace, surgeons' expertise. Future improvements should include diverse datasets, racial representation, aging features, and address privacy concerns. The ideal proportional ranges from senior plastic surgeons enhance its clinical relevance for personalized decision-making. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Purpose The purpose of this study is to delve into the intricacies of consumer shopping behavior within the metaverse, aiming to uncover how virtual environments influence decision-making processes. By examining psychological, social and technological factors, this study seeks to provide a comprehensive understanding of consumer experiences in digital realms and to propose a forward-looking research agenda. Design/methodology/approach Using a systematic literature review (SLR) grounded in the Theory, Context, Characteristics and Methods (TCCM) framework, complemented by thematic analysis, this study scrutinizes 96 articles sourced from Scopus, spanning the period from 2003 to 2024. Findings The metaverse engenders unique consumer behaviors, driven by immersive experiences, personalization and social presence. Virtual shopping fosters heightened engagement, loyalty and satisfaction, with emotional and ethical considerations significantly influencing purchase decisions. Key themes such as virtual economies, psychological ownership and trust underscore the delicate balance between digital convenience and privacy concerns. Notably, consumers exhibit impulse and exploratory tendencies, with a marked inclination toward prosocial and ethical choices. Practical implications The findings of this study provide actionable insights for businesses to leverage immersive technologies, address privacy concerns and foster consumer trust. Strategies emphasizing personalization, ethical consumption and virtual asset ownership are pivotal in enhancing consumer engagement in digital environments. Originality/value This study pioneers the integration of emerging theories with practical insights, advancing the understanding of virtual commerce dynamics. This study elucidates the evolving interplay between technology and consumer behavior, offering a strategic application in metaverse.

The convergence of artificial intelligence (AI) and 3D magnetic resonance imaging (MRI) is transforming orthopedic practice by overcoming traditional diagnostic limitations. This review synthesizes current advancements and future directions of AI-assisted 3D MRI analysis in orthopedics. Through critical evaluation of technical frameworks and clinical literature, we examine AI algorithms (including 3D CNNs and transformers), accelerated MRI acquisition techniques, and solutions for data heterogeneity and computational efficiency. Our analysis confirms that AI significantly enhances fracture classification accuracy, achieves exceptional segmentation precision for bone and cartilage structures, and reduces surgical complications through personalized planning and real-time navigation. Emerging strategies like federated learning address privacy concerns, while lightweight architectures optimize clinical deployment. Persistent challenges include data scarcity, model interpretability, and integration into healthcare systems. Future progress hinges on standardized multi-center validation, biomechanical simulation integration ("digital twins"), and regulatory alignment. AI-assisted 3D MRI promises to advance precision orthopedics but requires concerted collaboration across computational, engineering, and clinical domains to realize its full translational potential.

Purpose This study investigates how AI anthropomorphism, consumer innovativeness and data privacy concerns influence user attitudes and behavioral intentions toward AI-powered travel planning tools, framed within the theory of planned behavior (TPB). It positions AI anthropomorphism as a positive subjective norm, data privacy concerns as a negative subjective norm and consumer innovativeness as a proxy for perceived behavioral control. Design/methodology/approach A structured survey was administered to 476 users of AI-powered large language models (LLMs) for travel-related tasks. Structural equation modeling (SEM) was used to examine the direct and mediating relationships among the constructs. Findings AI anthropomorphism significantly enhances consumer innovativeness, indirectly promoting favorable attitudes and adoption intentions. However, it does not directly influence attitudes or intentions without the mediating effects of innovativeness and attitudes. Data privacy concerns negatively affect user attitudes but do not directly impede behavioral intentions, operating indirectly through cognitive evaluations. Practical implications Theoretically, this study refines TPB by showing that AI-related social cues and risk factors operate through indirect pathways. Practically, the findings inform the design of AI travel tools, suggesting features like adaptive AI personas, explainable suggestions and co-creative itinerary tools to engage innovative users and address privacy concerns. Originality/value This study extends TPB by integrating AI-specific constructs and psychological factors in tourism AI research. It offers a theory-driven and psychologically grounded model of AI adoption in tourism.

Background and Aim: As digital payments become more prevalent, users are shifting from third-party payment platforms (WeChat Pay, Alipay) to E-CNY, China’s central bank digital currency. However, despite its government backing, E-CNY adoption remains limited. This study aims to examine user switching behavior by applying the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to identify the key factors influencing adoption. Materials and Methods: A total of 308 valid respondents with experience using both E-CNY and third-party payment platforms participated in an online survey. The research was conducted in three stages: (1) a pilot study to test measurement reliability, (2) a descriptive analysis of user demographics, and (3) Structural Equation Modeling (SEM) to test the relationships between variables affecting switching behavior. Results: The findings indicate that government influence and social influence have the strongest positive impact on switching behavior. Network externalities, perceived ease of use, and perceived usefulness also encourage adoption, while privacy concerns negatively affect switching behavior. Interestingly, trust does not significantly influence user decisions, suggesting that convenience and usability matter more than trust in digital payments. Conclusion: To increase E-CNY adoption, policymakers should enhance usability, address privacy concerns, provide financial incentives, and expand merchant acceptance. Additionally, integrating E-CNY with existing payment platforms and leveraging peer influence will further encourage user switching.

Ear-EEG, as a special type of signal collected within the ear canal, offers advantages such as a relatively enclosed acquisition environment and reduced susceptibility to motion artifacts. Its signal-to-noise ratio is comparable to that of scalp EEG. However, its amplitude is generally lower, and the high energy consumption of previous devices has limited its long-term use. Additionally, the multi-channel acquisition method has increased the time required for electrode configuration, complicating the acquisition process. More importantly, past research has predominantly focused on the electrodes themselves, lacking the development of a complete acquisition system. These factors have restricted the widespread application of ear-EEG. To address these issues, this paper aims to develop a low-power, low-noise, real-time single-channel ear-EEG acquisition system. The system employs an integrated design, incorporating universal acquisition electrodes, a miniaturized wearable device for signal acquisition, processing, and transmission, and accompanying software for real-time data display and storage. This ensures convenient daily wear, enhances user experience, and improves the efficiency and quality of ear-EEG acquisition. Three experiments were designed to validate the system: a signal-to-noise ratio test, an alpha wave eye-opening and closing test, and an auditory steady-state response test, demonstrating its application value in scenarios requiring high signal quality, such as medical diagnosis and health monitoring.Clinical RelevanceThis system is designed for real-time monitoring of human ear-EEG signals. It not only ensures the accuracy of signal acquisition but also significantly enhances the concealment and convenience of use. In healthcare scenarios, the system helps address privacy concerns that patients may encounter when wearing medical devices outside the hospital, making daily medical monitoring and personalized health services feasible.

Building on an extended DeLone-McLean model, the current study investigates the influence of IS quality attributes, privacy concerns, super app reputation, and convenience on users' intention to continue using the Mini-Apps. The study tested hypotheses using the Structural Equation Modelling (SEM) technique. Data were collected from 289 experienced Vodacom Mpesa users in Tanzania. The study found that system quality, service quality, reputation, and satisfaction positively and significantly influence the users' intention to continue using mini-apps, while privacy concerns have adverse effects. Also, the reputation of the super apps (i.e., Vodacom Mpesa) moderates the influence of system quality and service quality on the user's intention to continue using Mini-Apps. At the same time, satisfaction plays a vital role in mediating the impact of information quality and system quality on the user's intention to continue using the Mini-Apps. Service providers and developers could use the findings to enhance IS Quality attributes and address privacy concerns and reputation to improve its continued mini-app usage. This study is among the early attempts in the African context to explore the moderation effects of super-app reputation and the mediation impact of user satisfaction on the intention to continue using mini-apps. It also extends DeLone-McLean's model by including privacy concerns, super apps' reputation, convenience, and system quality attributes to enhance mini-app continuance usage.

Mushroom farming is a vital sector in Jordan's agriculture (set to reach 1,060 metric tons by 2026). This study aims to revolutionize mushroom farming through the integration of the Internet of Things (IoT) and artificial intelligence (AI) capabilities through machine learning and the generative pre-trained transformer-4omni (GPT-4o) most recent model. A comprehensive hardware and software architecture was developed after training three machine vision models (Visual Geometry Group 16 - VGG16, Inception Third Version - InceptionV3, and Residual Network - ResNet50) and integrating GPT-4o for sound and text recommendations and an interactive dashboard with the alarming system. A robust dataset of (n = 4,755) images was meticulously processed, achieving the highest classification accuracy of 91.5% with ResNet50. The system’s static and real-time vision capabilities were validated with a prototype, integrating sensors for temperature, humidity, and gas levels. Unlike previous studies, this research uniquely combines IoT and AI with GPT-4o, providing intelligent recommendations and visual dashboards. The results demonstrated significant improvements, with practical applications extending to open-source tools for global use. Future research could explore drone integration and address privacy and security concerns. This study sets a new standard in agricultural technology, offering a scalable, efficient solution for modern mushroom farming and the potential to extend to further applications.

BackgroundThe burgeoning rise in social media use has revolutionized information dissemination, rendering social media a vital tool for promoting health campaigns and enhancing 2-way health communication between senders and users. Health planners and policy makers consider social media platforms (SMPs) vital for transferring useful health information to the public. However, there are important concerns about the decision makers’ perceptions of the evolving role of social media in health promotion and education campaigns.ObjectiveThis qualitative study explored how decision makers perceive the role of social media in health promotion and education. We aimed to shed light on strategic efficacy, real-world challenges, and valuable prospects of using social media for health communication.MethodsWe adopted a qualitative research method involving in-depth, semistructured, face-to-face interviews. We included 13 participants from government and private health care sectors in the Al-Qassim region of Saudi Arabia, who were key players and decision makers in health care programs and reforms. Data were recorded, transcribed verbatim, and analyzed using thematic analysis to identify key themes and patterns.ResultsFive main themes were identified: (1) use of social media (frequency, type of content, target audience, purpose of communication), (2) perceptions of decision makers (how social media influences public health behavior), (3) benefits, (4) challenges, and (5) implications for future use. Participants recognized the positive role of SMPs in spreading health information, particularly in health promotion and awareness campaigns. Communication emerged as a key concept, and WhatsApp, X (Twitter), and Facebook were recognized as major platforms for digital health literacy. The participants used these applications extensively for communication with colleagues, patients, and the public, intending discussion, information exchange, and health promotion campaigns. Content inaccuracy and reliability were identified as major challenges. Furthermore, misinformation and social inequalities were identified as barriers to effective communication. Participants suggested that social media influencers play a more effective role in information dissemination than the health care staff. Far-reaching audiences, visually appealing and engaging content using videos and graphics, and assessing campaign effectiveness using metrics, such as views, shares, likes, and comments, were recognized as major benefits of social media. Participants stressed the promising role of social media in the future as technological advancements in eHealth could revolutionize health care.ConclusionsSMPs play a vital role in sharing information about health-related initiatives. This research highlights the complexities and potential challenges of using social media for health promotion in Saudi Arabia. It emphasizes the need to develop strategies to combat misinformation, address privacy and confidentiality concerns, and ensure compliance with legal and ethical standards. Encouraging communication among key stakeholders, including health promotion experts, government organizations, social media companies, and the general public, can help establish effective guidelines and protocols to overcome the challenges.

This study investigates public perceptions and engagement with mobile health (mHealth) across eight European countries: Italy, the Netherlands, France, Germany, Spain, Poland, Romania, and Hungary. The focus is on attitudes toward health app usage and data sharing, addressing data privacy and security concerns while highlighting generational and educational differences. A cross-sectional survey was conducted with 6581 participants from the selected countries. The survey assessed current usage of health apps, interest in future use, willingness to share health data, and concerns about data privacy. Demographic factors such as age, education level, and geographical location were analyzed to determine their influence on mHealth engagement. The survey revealed that 21.87% of respondents currently use health apps, while 42.71% expressed interest in future use. Regarding data sharing, 52.82% were willing to share health data with healthcare providers, and 25.48% with public and private research institutions. However, concerns about data misuse (72.34%) and hacking (63.68%) were prevalent. Significant generational differences emerged, with older generations showing lower adoption rates of health apps. Education level was a key factor; individuals with tertiary education were more likely to use health apps and demand transparency. The findings emphasize the need for targeted strategies to improve digital literacy, address privacy concerns, and ensure equitable access to mHealth technologies across Europe. Tailored interventions are essential to bridge generational and educational gaps in mHealth engagement while fostering trust in data security measures.

The origin–destination (OD) matrix describes traffic flow information between regions. It is a critical input for intelligent transportation systems (ITS). However, obtaining the OD matrix remains challenging due to high costs and privacy concerns. Synthetic data, which have the same statistical distribution of real data, help address privacy issues and data scarcity. Based on Generative Adversarial Networks (GAN), OD matrix generation models, which can effectively generate a synthetic OD matrix, help to address the challenge of obtaining OD matrix data in ITS research. However, existing OD matrix generation methods can only handle with tens of nodes. To address this challenge, this study proposes the Origin–Destination Progressive Growing Generative Adversarial Networks (OD-PGGAN) for large-scale OD matrix generation task which adapt the PGGAN architecture. OD-PGGAN adopts a progressive learning strategy to gradually learn the structure of the OD matrix from a coarse to fine scale. OD-PGGAN utilizes multi-scale generators and discriminators to perform generation and discrimination tasks at different spatial resolutions. OD-PGGAN introduces a geography-based upsampling and downsampling algorithm to maintain the geographical significance of the OD matrix during spatial resolution transformations. The results demonstrate that the proposed OD-PGGAN can generate a large-scale synthetic OD matrix with 1024 nodes that have the same distribution as the real sample and outperforms two classical methods. The OD-PGGAN can effectively provide reliable synthetic data for transportation applications.

Volume 102 of the UTMJ arrives at a transformative moment in medicine, where rapid technological innovations are reshaping clinical practice, ethics, and healthcare delivery. As advancements in artificial intelligence, digital therapeutics, and remote monitoring redefine patient care, this issue explores the intricate interplay between technology and traditional medical practice, all while addressing the ethical, educational, and clinical challenges that arise. Our contributors offer a diverse collection of scholarly articles that not only probe critical clinical questions but also illuminate how technology is revolutionizing our understanding of health. We begin with “Cognitive Behavioural Therapy Outcomes for Clinical Perfectionism: A Scoping Review,” that examines the role of online and in-person CBT interventions to reduce the burdens of perfectionism. This study highlights how technology-driven approaches in modern medicine can be just as effective as traditional in-person methods while offering advantages in accessibility and scalability, addressing common barriers such as geographical constraints and limited resources. Complementing this perspective is “Moral Reasoning and Development in Medical School: A Literature Review,” which explores the education and evolution of ethical decision-making in contemporary medical learners. This review maps the trajectory of moral development in trainees, explaining that this decline has been linked to an educational approach that emphasizes compliance over critical engagement, a diminished focus on reflective practices, and a hidden curriculum that may conflict with formally taught ethical values. We highlight the need for robust ethics education that prepares future physicians to navigate the dilemmas posed by modern innovations in medicine, including AI and big data in healthcare. Volume 102 is further enriched by two compelling case reports, “A Bulky Primary Retroperitoneal Diffuse Large B-Cell Lymphoma: A Case Report” and “Atypical Chest Pain as a Prelude to Cancer: An Uncommon Presentation of Adenoid Cystic Carcinoma of the Parotid Gland.” This report emphasizes the challenges of diagnosing rare tumors but also highlights how modern medical technology, from enhanced imaging techniques to innovative diagnostic algorithms, plays a crucial role in discovering subtle signs of disease and informs management approaches. Bridging theoretical discussions of ethics and digital medicine with clinical practice, Volume 102 features two in-depth interviews that capture the spirit of technological innovation in medicine. We are honored to present an interview with Dr. Françoise Baylis, a luminary in bioethics whose work challenges and expands the ethical frameworks governing emerging technologies in gene editing and healthcare policy. Equally compelling is our conversation with Dr. Devin Singh, one of Canada’s pioneering physicians in clinical artificial intelligence. His insights as an emergency physician, educator, and entrepreneur provide a forward-looking perspective on harnessing AI to enhance clinical decision-making, address privacy concerns, and navigate the evolving regulatory landscape. Volume 102 of the UTMJ is more than a collection of academic articles; it is a reflection on the convergence of technology and medicine – a call to embrace innovation while rigorously examining its implications. We extend our deepest gratitude to the authors, reviewers, and editorial board for their intellectual contributions. To our readers, we hope this issue ignites curiosity, stimulates critical discourse, and inspires groundbreaking approaches that will shape the future of healthcare. Welcome to Volume 102 – a beacon for those committed to the relentless pursuit of knowledge, ethical practice, and technological excellence in medicine. Sincerely, David Chen and Alina Sami Editors-in-Chief University of Toronto Medical Journal

Introduction: Nurses’ understanding and perceptions of telemedicine are vital, as they are frequently the main users and implementers of these technologies in healthcare environments. With strong knowledge of telemedicine, nurses can utilize it effectively to enhance patient outcomes, optimize workflows, and ease pressure on healthcare resources. Moreover, positive attitudes among nurses can significantly boost adoption rates, as they are more inclined to support and champion telemedicine when they recognize its advantages for patient care and their own professional routines. Methodology: The cross-sectional study was conducted among 201 staff nurses. The data were collected using a self-structured questionnaire after thorough review of the literature. Descriptive and inferential statistics was used to analyze the characteristics of the participants. Results: This study investigated the demographics, knowledge, and attitudes of healthcare participants toward telemedicine, focusing on staff readiness for its adoption. Most respondents were female (67.2%), young adults (86.6%), and bachelor’s degree holders (52.7%) and identified as middle class (73.6%). Telemedicine awareness was high (89.1%), though fewer participants understood its infrastructure (50.7%). Knowledge varied by gender, experience, and income, with males and those with greater experience or income scoring higher. Attitudes were mostly positive, especially about telemedicine’s impact on workload and error reduction, though 64.6% expressed data privacy concerns. These results highlight the importance of targeted training to bridge knowledge gaps and address privacy concerns, supporting broader telemedicine adoption in health care. Conclusion: While telemedicine is a relatively new and developing practice in many countries, it shows strong potential to enhance healthcare services. This is especially valuable during pandemics, as it enables effective health care while supporting social distancing. Additionally, participants in this study demonstrated solid knowledge and a positive attitude toward telemedicine.

With the rise of virtual avatars in professional, educational, and recreational settings, this study investigates how different avatar types—varying in realism, gender, and identity—affect user perceptions of embodiment, acceptability, technostress, privacy, and preferences. Two studies were conducted with 42 participants in Study 1 and 40 in Study 2, including professionals and students with varying VR experiences. In Study 1, participants used pre-assigned avatars they could control during interactions. In Study 2, an interviewer used different avatars to interact with participants and assess their impact. Questionnaires and correlation analyses measured embodiment, technostress, privacy, and preference variations across contexts. Results showed that hyper-realistic avatars resembling the user enhanced perceived embodiment and credibility in professional and educational settings, while non-realistic avatars were preferred in recreational contexts, particularly when interacting with strangers. Technostress was generally low, though younger users were more sensitive to avatar appearance, and privacy concerns increased when avatars were controlled by others. Gender differences emerged, with women expressing more concern about appearance and men preferring same-gender avatars in professional environments. These findings highlight the need for VR platform designers to balance realism with user comfort and address privacy concerns to encourage broader adoption in professional and educational applications.

This study proposes an innovative AI-driven framework for evaluating and optimizing omnichannel retail marketing effectiveness to address challenges in integrating multiple retail channels and leveraging data for strategic decision-making. The research develops a comprehensive framework integrating big data analytics and advanced AI techniques, including reinforcement learning and graph neural networks. The framework combines diverse data sources, employs sophisticated algorithms for analysis, and utilizes adaptive optimization methods across channels. Validation uses controlled experiments and a case study with GlobalMart retail corporation. Experimental results demonstrate significant improvements in key performance indicators, including a 23.7% increase in sales revenue and a 27.6% boost in marketing ROI compared to traditional methods. The GlobalMart case study showed substantial enhancements in customer segmentation accuracy (37%), campaign conversion rates (28%), and online-to-offline integration (42%). The proposed framework offers retailers a powerful tool for marketing optimization in complex omnichannel environments, though future research should explore its adaptability to emerging technologies and address privacy concerns. Retailers can leverage this framework to enhance data-driven marketing strategies, improve resource allocation, and deliver seamless customer experiences across all touchpoints.