Search Results (825)

Background/Objectives: The emergence of artificial intelligence (AI) has revolutionized the healthcare industry. However, its integration into clinical practices raises ethical and practical concerns. This study aims to explore ethical and practical considerations perceived by physicians and nurses in Saudi Arabia. Methods: It employed a cross-sectional design with 400 physicians and nurses, using a pre-established online questionnaire. Descriptive data were analyzed through means and standard deviations, while inferential statistics were performed using the independent samples t-test. Results: Most participants were male (57%) and physicians (73.8%), with most employed in governmental organizations (87%). The participants’ use and awareness of AI was low, as 34.0% said they had never used it, but 74.5% of respondents were willing to use AI in clinical practices. Also, 80.5% of participants were aware of the AI benefits, and 71.0% had background knowledge about the ethical concerns related to AI’s implementation in their clinical practices. Moreover, (62.0%) of respondents recognized the applicability of AI in their specialty. Key findings revealed significant concerns: participants perceived a lack of skills to effectively utilize AI in clinical practice (mean = 4.04) and security risks such as AI manipulation or hacking (mean = 3.83). The most pressing ethical challenges included AI’s potential incompatibility with all populations and cultural norms (mean = 3.90) and uncertainty regarding responsibility for AI-related errors (mean = 3.84). Conclusions: These findings highlight substantial barriers that hinder the effective integration of AI in clinical practices in Saudi Arabia. Addressing these challenges requires leadership support, specific training initiatives, and developing practical strategies tailored to the local context. Future research should include other healthcare professionals and qualitatively explore further underlying factors influencing AI adoption. Full article

The evolving paradigm of precision medicine is redefining the landscape of orthobiologic therapies by moving beyond traditional diagnosis-driven approaches toward biologically tailored interventions. This review synthesizes current evidence supporting precision orthobiologics, emphasizing the significance of individualized treatment strategies in musculoskeletal regenerative medicine. This narrative review synthesized literature from PubMed, Embase, and Web of Science databases (January 2015–December 2024) using search terms, including ‘precision medicine,’ ‘orthobiologics,’ ‘regenerative medicine,’ ‘biomarkers,’ and ‘artificial intelligence’. Biological heterogeneity among patients with ostensibly similar clinical diagnoses—reflected in diverse inflammatory states, genetic backgrounds, and tissue degeneration patterns—necessitates patient stratification informed by molecular, genetic, and multi-omics biomarkers. These biomarkers not only enhance diagnostic accuracy but also improve prognostication and monitoring of therapeutic responses. Advanced imaging modalities such as T2 mapping, DTI, DCE-MRI, and molecular PET offer non-invasive quantification of tissue health and regenerative dynamics, further refining patient selection and treatment evaluation. Simultaneously, bioengineered delivery systems, including hydrogels, nanoparticles, and scaffolds, enable precise and sustained release of orthobiologic agents, optimizing therapeutic efficacy. Artificial intelligence and machine learning approaches are increasingly employed to integrate high-dimensional clinical, imaging, and omics datasets, facilitating predictive modeling and personalized treatment planning. Despite these advances, significant challenges persist—ranging from assay variability and lack of standardization to regulatory and economic barriers. Future progress requires large-scale multicenter validation studies, harmonization of protocols, and cross-disciplinary collaboration. By addressing these limitations, precision orthobiologics has the potential to deliver safer, more effective, and individualized care. This shift from generalized to patient-specific interventions holds promise for improving outcomes in degenerative and traumatic musculoskeletal disorders through a truly integrative, data-informed therapeutic framework. Full article

The increasing adoption of industrialized offsite construction (IOC) offers substantial benefits in efficiency, quality, and sustainability, yet presents persistent challenges related to data fragmentation, real-time monitoring, and coordination. This systematic review investigates the transformative role of artificial intelligence (AI)-enhanced digital twins (DTs) in addressing these challenges within IOC. Employing a hybrid re-view methodology—combining scientometric mapping and qualitative content analysis—52 relevant studies were analyzed to identify technological trends, implementation barriers, and emerging research themes. The findings reveal that AI-driven DTs enable dynamic scheduling, predictive maintenance, real-time quality control, and sustainable lifecycle management across all IOC phases. Seven thematic application clusters are identified, including logistics optimization, safety management, and data interoperability, supported by a layered architectural framework and key enabling technologies. This study contributes to the literature by providing an early synthesis that integrates technical, organizational, and strategic dimensions of AI-driven DT implementation in IOC context. It distinguishes DT applications in IOC from those in onsite construction and expands AI’s role beyond conventional data analytics toward agentive, autonomous decision-making. The proposed future research agenda offers strategic directions such as the development of DT maturity models, lifecycle-spanning integration strategies, scalable AI agent systems, and cost-effective DT solutions for small and medium enterprises. Full article

Innovative technologies have been helping to improve comfort and safety at work in high-risk sectors for years. The study analysed the impact, along with an assessment of potential implementations (opportunities and limitations) of innovative technological solutions for improving occupational safety in two selected sectors of the economy: mining and construction. The technologies evaluated included unmanned aerial vehicles and inspection robots, the Internet of Things and sensors, artificial intelligence, virtual and augmented reality, innovative individual and collective protective equipment, and exoskeletons. Due to the extensive nature of the obtained materials, the research description has been divided into two articles (Part I and Part II). This article presents the first three technologies. After the scientific literature from the Scopus database was analysed, some research gaps that need to be filled were identified. In addition to the obvious benefits of increased occupational safety for workers, innovative technological solutions also offer employers several economic advantages that affect the industry’s sustainability. Innovative technologies are playing an increasingly important role in improving safety in mining and construction. However, further integration and overcoming implementation barriers, such as the need for changes in education, are needed to realise their full potential. Full article

Research on corporate failure prediction is focused on increasing the model’s statistical accuracy, most recently via the introduction of a variety of machine learning (ML)-based models, often overlooking the practical appeal and potential adoption barriers in the context of corporate management. This literature review compares ML models with the classic, widely accepted Altman Z-score through a technology adoption lens. We map how technological features, organizational readiness, environmental pressure and user perceptions shape adoption using an integrated technology adoption framework that combines the Technology–Organization–Environment framework with the Technology Acceptance Model. The analysis shows that Z-score models offer simplicity, interpretability and low cost, suiting firms with limited analytical resources, whereas ML models deliver superior accuracy and adaptability but require advanced data infrastructure, specialized expertise and regulatory clarity. By linking the models’ characteristics with adoption determinants, the study clarifies when each model is most appropriate and sets a research agenda for long-horizon forecasting, explainable artificial intelligence and context-specific model design. These insights help managers choose failure prediction tools that fit their strategic objectives and implementation capacity. Full article

This systematic literature review examines the integration of Artificial Intelligence (AI) and Extended Reality (XR) technologies in language education, synthesizing findings from 32 empirical studies published between 2017 and 2024. Guided by the PRISMA framework, we searched four databases—ERIC, Web of Science, Scopus, and IEEE Xplore—to identify studies that explicitly integrated both AI and XR to support language learning. The review explores publication trends, educational settings, target languages, language skills, learning outcomes, and theoretical frameworks, and analyzes how AI–XR technologies have been pedagogically integrated, and identifies affordances, challenges, design considerations, and future directions of AI–XR integration. Key integration strategies include coupling AI with XR technologies such as automatic speech recognition, natural language processing, computer vision, and conversational agents to support skills like speaking, vocabulary, writing, and intercultural competence. The reported affordances pertain to technical, pedagogical, and affective dimensions. However, challenges persist in terms of technical limitations, pedagogical constraints, scalability and generalizability, ethical and human-centered concerns, and infrastructure and cost barriers. Design recommendations and future directions emphasize the need for adaptive AI dialogue systems, broader pedagogical applications, longitudinal studies, learner-centered interaction, scalable and accessible design, and evaluation. This review offers a comprehensive synthesis to guide researchers, educators, and developers in designing effective AI–XR language learning experiences. Full article

Species of the Ferula genus are known for their traditional medicinal applications against diverse illnesses. Our previous study was the first to suggest the cholinesterase inhibitory activity of Ferula persica L. However, the neuroprotective efficacy of therapeutic molecules is often limited by their ability to cross the blood–brain barrier (BBB) and reach the brain. In the present study, the BBB permeability of the main molecules present in the aerial parts and roots of F. persica L. extracted under optimum conditions was assessed using two well-established methods: the parallel artificial membrane permeability assay (PAMPA) and the HBMEC cell culture in vitro model. The results demonstrated a high permeability of several neuroprotective compounds, such as apigenin, diosmetin, and α-cyperone. Additionally, the neuroprotective potential of F. persica extracts was evaluated using SH-SY5Y neuron-like cells exposed to different insults, including oxidative stress (H₂O₂), excitotoxicity (L-glutamate), and Aβ1-42 peptide toxicity. However, none of the obtained extracts provided significant protection. This study highlights the importance of in vitro cell culture models for a better understanding of BBB permeability mechanisms and reports the tentative identification of newly formed sulfated metabolites derived from the metabolism of ferulic acid, apigenin, and diosmetin by HBMEC cells. Full article

Trustworthy product quality data forms the foundation of digital and distributed manufacturing, yet current centralized product quality management (PQM) systems remain vulnerable to data manipulation, traceability breaks, single points of failure, and related adverse effects. To clarify how blockchain can address these weaknesses, this paper presents a systematic review of blockchained product quality management (BPQM). Firstly, the paper groups the architectures and models related to BPQM and proposes an ISA 95-aligned reference framework that secures a real-time quality data exchange. Secondly, seven key BPQM enablers are analyzed, including (1) visual intelligence-based quality inspection, (2) cyber–physical twinning and parallel control of manufacturing systems, (3) blockchained agent modeling and secure data sharing, (4) multi-level blockchain mapping, (5) smart contract-based decentralized system configuration and operation, (6) artificial intelligence-based decentralized BPQM applications, and (7) traceability of process coordination and control. Thirdly, through analysis of social barriers and technological challenges, four research directions are identified, namely, (1) optimal granularity of data in system configuration; (2) smart contracts for self-organizing intelligence; (3) balancing system security, cost, and performance; and (4) interoperability and integration with legacy systems. It is expected that this paper lays a solid foundation for the practical use of blockchain in PQM engineering. Full article

Veterinary vaccines are essential tools for controlling infectious and zoonotic diseases, safeguarding animal welfare, and ensuring global food security. However, conventional vaccines are hindered by cold-chain dependence, thermal instability, and logistical challenges, particularly in low- and middle-income countries (LMICs). This review explores next-generation veterinary vaccines, emphasizing innovations in thermostability and delivery platforms to overcome these barriers. Recent advances in vaccine drying technologies, such as lyophilization and spray drying, have improved antigen stability and storage resilience, facilitating effective immunization in remote settings. Additionally, novel delivery systems, including nanoparticle-based formulations, microneedles, and mucosal routes (intranasal, aerosol, and oral), enhance vaccine efficacy, targeting immune responses at mucosal surfaces while minimizing invasiveness and cost. These approaches reduce reliance on cold-chain logistics, improve vaccine uptake, and enable large-scale deployment in field conditions. The integration of thermostable formulations with innovative delivery technologies offers scalable solutions to immunize livestock and aquatic species against major pathogens. Moreover, these strategies contribute significantly to One Health objectives by mitigating zoonotic spillovers, reducing antibiotic reliance, and supporting sustainable development through improved animal productivity. The emerging role of artificial intelligence (AI) in vaccine design—facilitating epitope prediction, formulation optimization, and rapid diagnostics—further accelerates vaccine innovation, particularly in resource-constrained environments. Collectively, the convergence of thermostability, advanced delivery systems, and AI-driven tools represents a transformative shift in veterinary vaccinology, with profound implications for public health, food systems, and global pandemic preparedness. Full article

Polyp retrieval following colorectal polypectomy remains a critical step for histopathological analysis and determining appropriate surveillance intervals. Despite reported retrieval rates exceeding 90% in the literature, significant heterogeneity persists in clinical practice, particularly for polyps < 10 mm, due to the lack of standardized retrieval methods. This review synthesizes current evidence on polyp retrieval techniques, the impact of lesion size, and device-specific considerations, including suction-based methods, retrieval nets, and other approaches such as the water-bolus and water-slider techniques. We also examine the clinical utility and limitations of the “resect and discard” and “diagnose and leave in situ” strategies, highlighting barriers to widespread implementation such as medico-legal risks, variability in optical diagnosis, and discrepancies across international guidelines. The integration of advanced imaging technologies, including high-definition endoscopy, virtual chromoendoscopy, and artificial intelligence-driven computer-aided diagnosis (CADx), represent promising tools to help in increasing the diagnostic accuracy of diminutive polyps. As post-polypectomy surveillance recommendations remain tethered to histological findings, this review underlines the need for updated, evidence-based frameworks that take into account technological advancements while ensuring diagnostic precision, cost-effectiveness, and patient safety in colorectal cancer prevention. Full article

Artificial intelligence (AI) is increasingly transforming the public sector, although the willingness of organizations to adopt such technologies varies widely. Existing models, such as the technology–organization–environment (TOE) model, highlight systemic drivers and barriers but overlook the individual-level factors that are also critical to successful adoption. To address this gap, we propose a decision model that combines the TOE model with the unified theory of acceptance and use of technology (UTAUT) and combines the dimensions of technology, organization, environment, and individual readiness. The model was developed using the Analytic Hierarchy Process (AHP) and supports group decision-making by combining the pairwise comparison matrices of multiple experts into a consolidated priority structure. Specifically, many expert judgments were used to create a group matrix for the four main categories and four additional group matrices for the criteria within each category. This structured approach allows for a systematic assessment of whether a public sector organization is ready for AI adoption. The results show the importance of both systemic factors (such as data, technology, innovation, and readiness for change) and individual factors (such as social influence and voluntariness of use). The final model provides a comprehensive and practical decision-making tool for public sector organizations to assess readiness, identify gaps, and guide the strategic adoption of AI. Full article

Service learning (SL) is at a pivotal moment as education systems worldwide confront the challenges and opportunities posed by artificial intelligence (AI) and digital technologies. This scoping review synthesizes regional perspectives on SL and examines the barriers to its implementation in higher education. This study adopts a methodological approach widely used in prior educational research, enriched with selected PRISMA processes, namely identification, screening, and eligibility, to enhance its transparency and rigor. A total of 101 peer-reviewed articles were analyzed, using a mixed methods approach. Results are presented for six regions, Africa, Asia, Latin America, Europe, North America, and Oceania, revealing context-specific constraints, such as technological infrastructure, policy frameworks, linguistic diversity, and socio-economic disparities. Common barriers across regions include limited faculty training, insufficient institutional support, and misalignment with community needs. AI is explored as a potential enabler of SL, not as an empirical outcome, but as part of a reasoned argument emerging from the documented complexity of SL implementation in the literature. Ethical considerations, including algorithmic bias, equitable access, and the preservation of human agency, are addressed, alongside mitigation strategies that are grounded in participatory design and community engagement. This review offers a comparative, context-sensitive understanding of SL implementation challenges, providing actionable insights for educators, policymakers, and researchers, aiming to integrate technology-enhanced solutions responsibly. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

Intelligent manufacturing research has focused on digital twins (DTs) due to the growing integration of physical and cyber systems. This study thoroughly explores the Internet of Things (IoT) as a cornerstone of DTs, showing its promise and limitations in intelligent shipbuilding digital transformation workshops. We analyze the progress of IoT protocols, digital twin frameworks, and intelligent ship manufacturing. A unique bidirectional digital twin system for shipbuilding workshops uses the Internet of Things to communicate data between real and virtual workshops. This research uses a steel-cutting workshop to demonstrate the digital transformation of the production line, including data collection, transmission, storage, and simulation analysis. Then, major hurdles to digital technology application in shipbuilding are comprehensively examined. Critical barriers to DT deployment in shipbuilding environments are systematically analyzed, including technical standard unification, communication security, real-time performance guarantees, cross-workshop collaboration mechanisms, and the deep integration of artificial intelligence. Adaptive solutions include hybrid edge-cloud computing architectures for latency-sensitive tasks and reinforcement learning-based smart scheduling algorithms. The findings suggest that IoT-driven digital transformation may modernize shipbuilding workshops in new ways. Full article

Background: Motor imagery (MI) is defined as the cognitive ability to simulate motor movements while suppressing muscular activity. The electroencephalographic (EEG) signals associated with lower limb MI have become essential in brain–computer interface (BCI) research aimed at assisting individuals with motor disabilities. Objective: This systematic review aims to evaluate methodologies for acquiring and processing EEG signals within brain–computer interface (BCI) applications to accurately identify lower limb MI. Methods: A systematic search in Scopus and IEEE Xplore identified 287 records on EEG-based lower-limb MI using artificial intelligence. Following PRISMA guidelines (non-registered), 35 studies met the inclusion criteria after screening and full-text review. Results: Among the selected studies, 85% applied machine or deep learning classifiers such as SVM, CNN, and LSTM, while 65% incorporated multimodal fusion strategies, and 50% implemented decomposition algorithms. These methods improved classification accuracy, signal interpretability, and real-time application potential. Nonetheless, methodological variability and a lack of standardization persist across studies, posing barriers to clinical implementation. Conclusions: AI-based EEG analysis effectively decodes lower-limb motor imagery. Future efforts should focus on harmonizing methods, standardizing datasets, and developing portable systems to improve neurorehabilitation outcomes. This review provides a foundation for advancing MI-based BCIs. Full article