Search Results (5,937)

The study explores the paradigm shift in education brought about by the introduction of generative artificial intelligence (AI) tools, focusing on educational stakeholders’ self-reported perceptions rather than observed changes in teaching or learning outcomes. We consider stakeholders’ views on AI-based technologies within the teaching–learning process. The current study uses a cross-sectional empirical survey design with a sample of N = 917 respondents, including teachers, students, administrators, and management. It examines the use of advanced AI technologies such as ChatGPT, Gemini, DeepSeek, and Grok, and stakeholders’ perceived connection between digital skills and classroom performance, student motivation, and critical thinking. We also discuss the ethical dilemmas and structural challenges that accompany this digital change. Inferential statistics, such as One-Way ANOVA and the Pearson Chi-Square test, show statistically significant differences in perceptions and regulatory expectations across organizational responsibilities. The findings contribute to understanding how advanced digitalization is perceived to reshape traditional academic roles, offering practical insights for creating effective, responsible, and sustainable teaching practices. Full article

Background/Objectives: The primary objective was to assess reference intervals for fetal Global Longitudinal Strain (GLS) using bidimensional speckle-tracking echocardiography (2D-STE), including only prospective studies specifically designed for this purpose. An additional objective was to evaluate studies’ methodological quality and reproducibility. Methods: The study is a systematic review registered at PROSPERO (CRD420251038889). Five electronic databases (Web of Science, Scopus, MEDLINE/PubMed, EMBASE and LILACS) were searched, from inception to May 2025. Prospective studies specifically designed to establish 2D-STE GLS reference intervals in low-risk singleton pregnancies with normal fetuses were included. Data were independently extracted by two reviewers. Risk of bias was assessed using an adapted tool, including study design and statistical and reporting methods. Results: After the initial identification of 187 records, nine studies published between 2012 and 2025 were included. There was marked heterogeneity among the studies. Four articles achieved high-quality scores (>70%) and three reported similar left ventricular (LV) GLS at 24 weeks (−22%). Right ventricular GLS absolute values were slightly lower than LV values. Regression models for both ventricles showed GLS absolute values decreased with gestation across studies. The 2D-STE algorithm (endocardial versus myocardial) was the main source of discrepancy between studies. Conclusions: High-quality prospective studies show a consistent pattern of biventricular GLS variation with gestational age. However, technical heterogeneity, lack of standardization, operator subjectivity and vendor-specific algorithm differences currently limit the applicability of the method. Multicentric studies with large sample sizes, standardized protocols and artificial intelligence-assisted tools are needed to consolidate this technique. Full article

Background: Point-of-care ultrasound (POCUS) use continues to expand across specialties caring for pediatric patients in the perioperative period, offering noninvasive, diagnostic information that complements clinical assessment in real time. Although many POCUS applications were developed and validated in adults, pediatric POCUS requires unique considerations. This review examines the current evidence of diagnostic POCUS within pediatric perioperative care. Methods: A structured search of PubMed was performed for literature published through December 2025, using terms related to point-of-care ultrasound, perioperative care, cardiac, lung, gastric, and airway ultrasound, as well as focused assessment with sonography in trauma (FAST). Priority was given to systematic reviews, meta-analyses, randomized controlled trials, clinical practice guidelines, and landmark observational studies, while supplemented by recent high-quality publications. Findings: POCUS in each of the following areas is discussed: cardiac, pulmonary, airway, gastric, and FAST. Topics for each of the areas include a general overview, considerations for devices and probe selection to optimize imaging, specific uses and applications of the imaging, and potential limitations. Conclusions: POCUS is a valuable tool to complement comprehensive clinical evaluation of children in the perioperative period. Widespread adoption will require additional pediatric normative data, standardized protocols, and structured training for providers. Future research should continue to validate POCUS algorithms and clarify the role of artificial intelligence in enhancing image interpretation and accessibility. Full article

In recent years, interest in artificial intelligence has grown significantly, particularly in the development of advanced computational models for supply chain decision-making. Inventory management is one of the areas in which intelligent algorithms can support demand forecasting, replenishment, stock control, and operational optimization under uncertainty. This study presents an updated systematic literature review of intelligent algorithms applied to inventory management. The review followed PRISMA 2020 guidelines and combined database searches in Scopus, ScienceDirect, Web of Science, IEEE Xplore, SpringerLink, Taylor & Francis, and complementary manual searching. The original search covering January 2020 to December 2024 was updated in July 2026 to include studies published or available online up to June 2026. After applying strict eligibility criteria, 37 primary studies with quantitative evidence were included. The updated corpus confirms the predominance of deep learning, reinforcement learning, and hybrid intelligent models, while also showing the recent emergence of Transformer-based, graph neural network, multi-agent reinforcement learning, and prescriptive analytics approaches. The most frequent application areas were inventory control, inventory optimization, replenishment decision-making, and demand forecasting. Reported improvements were mainly associated with cost efficiency, service level, stockout reduction, and system performance; however, the magnitude of improvement varied across algorithms, data sources, sectors, and simulation or real-world settings. Overall, intelligent algorithms represent a relevant tool for improving inventory management, but their adoption requires careful validation, transparent reporting, and alignment with the operational context. Full article

Generative artificial intelligence has been rapidly adopted by university students, yet there is limited evidence describing how and why medical students use it, particularly in regional settings. This cross-sectional study examined the use of generative artificial intelligence for learning and assessment among medical students at James Cook University, a regional Australian medical school with three North Queensland campuses. All enrolled students (Years 1–6) were invited to complete a 24-item online survey; closed-ended items were analysed using frequency and bivariate analyses by year level and gender, with correction for multiple comparisons, and open-ended responses were analysed using qualitative content analysis. In total, 438 students responded. Eighty percent reported using artificial intelligence for their studies or assignments, and 95% supported its use in medical education in some capacity. The most common uses were explaining concepts (56%) and answering medical content questions (54%). Pre-clinical students reported greater study-related use, whereas clinical year students reported greater assessment-related use. Male students also reported higher levels of use, willingness to pay, and trust in AI tools. Although uptake was high, trust was moderate, and students expressed concerns about professionalism and critical thinking. Medical schools should provide explicit guidance on acceptable use, incorporate artificial intelligence literacy training and ethical use guidelines, and redesign assessment to protect the skills students perceive to be most at risk. Full article

The intersection of artificial intelligence (AI) and gamification in language education has attracted increasing attention, but its link to sustainability is still largely unexamined. This study looks at whether and how much this area of research tackles two aspects of sustainability: the ability of AI-driven gamified tools promoting lasting independent language learning (pedagogical sustainability) and their potential to reduce educational inequalities in line with Sustainable Development Goal 4 (SDG 4) (social sustainability). Using a sequential explanatory mixed-methods design, a bibliometric analysis of 105 documents retrieved from Scopus and Web of Science (WoS) (2018–2026), processed with bibliometrix R package (4.6.0) and VOSviewer (1.6.20), and a thorough qualitative content analysis of 27 studies were combined. The bibliometric mapping uncovered four thematic clusters around gamification design, motivational theory, serious games, and AI-driven vocabulary learning. No sustainability-related terms had enough density to form a clear cluster. The qualitative analysis showed that SDG 4 is missing from all 27 reviewed documents. Only three (11.1%) operationalizing pedagogical sustainability and mediation as a CEFR competence are absent. These findings suggest that sustainability is still on the fringe of this field and support a research agenda focusing on long-term measurement, self-directed learning support, low-resource design and mediation in AI-driven gamified environments. Full article

Generative artificial intelligence has begun to answer for the collection in the institution’s own voice, yet commentary has accumulated far faster than working systems have been documented, and no prior review identified by this search has consolidated and critically read the record of what has actually been deployed. Using a single open bibliographic index (OpenAlex)—chosen for reach and reproducibility, at the acknowledged cost of studies indexed only elsewhere—and a scoping protocol, this review narrows the literature to the systems genuinely placed in a working museum or gallery and observed in use, and maps them onto a function-based taxonomy. From a de-duplicated pool of more than eight thousand records, twenty-eight works describing twenty-seven distinct deployments are included. Three centers of gravity emerge: conversational guides that address the visitor, co-creative installations that make the act of generation the exhibit, and behind-the-scenes tools that read and catalog the collection. Against that map, the review weighs the evidence, confining every judgment of how well a system worked to the six studies that meet the review’s predefined evaluation-strength criteria. The result is a field whose map is clear while its proof is thin: the deployments can be named and sorted with confidence, but evidence that any delivers what it promises rests on a handful of cases, and the one production-scale system is also the only one tested at scale. Education, access, and audience understanding are almost absent from the record this search retrieved. The review closes with evidence-calibrated guidance for institutions weighing a deployment—a decision aid, a set of questions to put to a vendor, and stage-by-stage recommendations for selection, implementation, and evaluation, each marked with the strength of the evidence behind it—and an agenda redirecting the field toward the civic work it has set aside. Full article

Background: Maintenance hemodialysis is characterized by repetitive changes in fluid distribution, blood pressure, and organ perfusion. Conventional clinical examination, empirical dry-weight adjustment, and biomarkers do not fully resolve the compartment-specific nature of congestion in this population. This narrative review reframes ultrasound-based volume assessment as a multimodal diagnostic problem involving pulmonary congestion, intravascular filling, systemic venous congestion, cardiac reserve, tissue response, and perfusion vulnerability. Methods: We synthesized clinically relevant evidence indexed in PubMed and Google Scholar for studies published between January 2016 and April 2026, prioritizing dialysis-specific randomized trials, prospective cohorts, systematic reviews, consensus statements, and methodological studies related to diagnostic ultrasound, Doppler-based congestion assessment, contrast-enhanced ultrasound, elastography, artificial intelligence, point-of-care ultrasound, and remote ultrasound monitoring. Results: Lung ultrasound currently has the strongest dialysis-specific evidence for detecting and tracking pulmonary congestion. Inferior vena cava ultrasound provides adjunctive information on intravascular filling and right-sided pressure but is not a surrogate for total body water. Echocardiographic parameters help characterize filling pressure and cardiac tolerance to fluid removal, whereas venous Doppler and the Venous Excess Ultrasound Score provide an emerging approach to systemic venous congestion. Elastography and contrast-enhanced ultrasound remain investigational tools for tissue characterization and perfusion vulnerability, while AI-assisted analysis, handheld point-of-care ultrasound, and tele-ultrasound may improve standardization, automated B-line quantification, and scalability. Conclusions: Different ultrasound modalities answer different diagnostic questions in maintenance hemodialysis. A compartment-specific framework integrating congestion, perfusion, and cardiac-reserve domains may better support individualized ultrafiltration planning, hemodynamic risk assessment, and future outcome-oriented research. Full article

Artificial intelligence (AI) is increasingly being integrated into medical practice, offering promising tools to improve diagnostic accuracy and clinical efficiency. In the field of breast pathology, AI applications, particularly those based on deep learning (DL) and machine learning (ML), are emerging as decision-support tools in both diagnostic and prognostic workflows. This review provides a comprehensive overview of current AI-based approaches, with a focus on their clinical utility in tumor detection, histological classification, biomarker assessment, and prediction of treatment response. In addition to summarizing available AI platforms, the review critically examines their level of clinical validation, regulatory status, and integration into routine practice. Key challenges are also discussed. Overall, AI is expected to play an increasingly important role in supporting pathologists and advancing precision medicine in breast cancer management. Full article

The rapid diffusion of generative artificial intelligence (GenAI) tools such as ChatGPT has unsettled established academic practices related to assessment, authorship, integrity, and disciplinary knowledge production. This qualitative repeated cross-sectional study examines how faculty and staff at a large public research university understood these changes in 2023 and 2024. The analysis draws on responses to the same open-ended survey question collected from independent respondent groups in 2023 (n = 104) and 2024 (n = 313). Responses were analyzed inductively through thematic analysis and subsequently interpreted using Disruptive Innovation Theory and Complex Adaptive Systems Theory. The analysis identified both continuity and change across the two datasets. Responses in 2023 emphasized uncertainty, threats to academic integrity, and defensive assessment redesign. Responses in 2024 more frequently described pedagogical experimentation, process-oriented assessment, and AI literacy as emerging academic and professional competencies. Concerns about authorship, equity, reliability, and inconsistent institutional guidance persisted across both years. The findings suggest that faculty and staff discourse shifted from primarily containing GenAI-related risks toward selectively integrating the technology into teaching and professional practice. However, because the study used independent cross-sectional samples, it does not establish individual change over time. The study contributes a theoretically informed account of institutional sensemaking during the first two years following ChatGPT’s public release and identifies strategies for balancing innovation, integrity, equity, and the human purposes of higher education. Full article

The diffusion of generative artificial intelligence has moved educational debate beyond access to new tools, placing teacher work, pedagogical mediation, assessment, and digital competence under renewed pressure. This article examines teachers’ perceptions, declared uses, and forms of engagement with generative AI in Gymnasien (academic-track secondary schools) and Gesamtschulen (comprehensive secondary schools) in North Rhine-Westphalia, Germany. The study is based on an anonymous and voluntary online questionnaire circulated through school leaderships. The operational database comprised 204 records, while the main analysis was based on 156 completed questionnaires. Closed-item responses were examined through descriptive statistics and interpreted through DigCompEdu and its AI-related supplement. The findings indicate an asymmetrical incorporation of generative AI across teachers’ work. Its presence is stronger in preparation, organisation, professional reflection, and risk awareness than in direct classroom mediation, process-oriented assessment, and institutionally supported student use. The strongest contrasts concern perceived student overreliance on AI-generated answers, the need to adapt assessment practices, limited school-level guidance, and weak use of AI-supported process monitoring. Rather than interpreting this imbalance as a simple delay in adoption, the article argues that teacher competence in relation to generative AI is produced through the interaction between professional judgement, pedagogical purposes, and institutional conditions. It therefore cannot be reduced to an individual technical attribute. Full article

The transition towards smart megacities has deeply integrated Artificial Intelligence (AI) with power–transport networks. While AI empowers complex operations like multi-network coordinated dispatch and emergency rescue, current algorithm-centric perspectives largely ignore its massive physical energy costs. Accordingly, this Perspective examines the dual role of AI, considering it not only as an intelligent decision-support tool but also as a potential source of additional stress on physical infrastructure. First, through a structured synthesis of the representative literature, we deconstruct the functional dependencies between algorithms and physical infrastructures, identifying how AI reshapes the operational paradigms of power, ground transport, and aerial networks under routine and emergency scenarios. We then introduce the concept of the “Computation–Energy Paradox.” Integrating conceptual analysis with a quantitative case study of a typical community, we illustrate a plausible failure mechanism: during extreme disasters, intensified AI invocation for emergency management generates surging computational loads, which paradoxically exacerbate power shortages and reduce the operating margin of already weakened systems. In addition, we analyze core engineering bottlenecks, including spatiotemporal computation–energy mismatches and physical constraints in extreme edge environments. To address these challenges, we outline a prospective roadmap encompassing lightweight emergency AI and computation–power-coordinated offloading mechanisms. Finally, the sustainable development of such systems suggests a paradigm shift: AI must evolve from a purely virtual algorithm into a physical component of an integrated compute–power–transport system. Full article

Proto-AGI describes the intermediate stage of artificial intelligence between narrow task-specific tools and fully autonomous general intelligence. This paper presents the first formal operationalisation of Proto-AGI traits within a cross-domain digital workflow for structural engineering and demonstrates it through a four-domain computational ecosystem applied to seismic vulnerability assessment. Viktor.ai processes cone penetration test data to stratify a three-layer soil profile, identifying a compressible intermediate stratum at 5 to 12 m depth with amplification characteristics in the 0.3 to 0.7 second period range, based on the depth and stiffness contrast of the weak layer rather than a formal site response analysis. The Fayaz RotD script computes orientation-independent RotD50 and RotD100 response spectra for two contrasting ground motion records: the near-fault Northridge record (RSN 1086, Mw 6.69) delivers RotD50 = 2.00 g and RotD100 = 2.79 g at the structural natural period of 0.41 s, a 39.6% directional uplift; the moderate-distance Kobe record (RSN 1107, Mw 6.9) delivers RotD50 = 0.591 g and RotD100 = 0.795 g at the same period. OpenSeesPy nonlinear dynamic analysis of a five-storey reinforced concrete frame produces peak inter-storey drifts of 0.45% and 0.34% under the two records respectively, both within the FEMA 356 Immediate Occupancy threshold of 1.0%. A 3.4-fold spectral demand difference produces only a 1.32-fold drift difference, reflecting the combined effects of frequency content, pulse characteristics, duration and nonlinear structural response under the two contrasting records. The Viktor.ai RC Section Analyzer yields a curvature ductility factor of 2.3 under ACI 318-25, identifying deformation capacity as the governing constraint under more severe future demands. These four findings form a causal chain connecting site conditions, spectral demand, structural response and sectional capacity that no single domain produces independently—the emergent ecosystem intelligence that defines Proto-AGI in structural engineering practice. Full article

Background and Objectives: Artificial intelligence (AI) is increasingly used in clinical dentistry, but its diagnostic accuracy for oral mucosal lesions based on clinical photographs remains insufficiently validated. This prospective observational study compared the Top-1 diagnostic accuracy of ChatGPT-4o and ChatGPT-5 in identifying normal anatomical variants and pathological oral mucosal lesions and evaluated their performance across anatomical sites. Materials and Methods: Seventy adults with either normal anatomical variants (n = 21) or pathological oral mucosal lesions (n = 49) were consecutively recruited at the Department of Dental Medicine, University Hospital of Split, Croatia. One standardized clinical photograph per patient was analyzed by ChatGPT-4o and ChatGPT-5 under image-only and image-plus-text conditions using identical prompts. The reference diagnosis was established by an oral medicine specialist, with histopathological examination (HPE) performed when clinically indicated. Diagnostic performance was assessed using Top-1 accuracy and McNemar’s test. Results: Both models showed low accuracy with image-only input, but performance improved significantly after clinical information was added (p < 0.001). Overall Top-1 accuracy increased from 19.0% to 69.0% for ChatGPT-4o and from 9.0% to 51.0% for ChatGPT-5. For normal anatomical variants, accuracy increased from 14.3% to 81.0% and from 14.3% to 76.2%, respectively. For pathological oral mucosal lesions, accuracy increased from 20.4% to 63.3% and from 6.1% to 40.8%, respectively. ChatGPT-4o showed numerically higher accuracy than ChatGPT-5, particularly for pathological oral mucosal lesions, but no statistically significant difference was found between the models in the corresponding paired comparisons. Conclusions: Diagnostic performance was limited with image-only input but improved substantially when standardized clinical information accompanied the images. The numerical differences between models, particularly for pathological oral mucosal lesions, may be clinically relevant but do not establish superiority or equivalence. Neither model can currently replace conventional clinical diagnosis, and AI should be regarded as a clinical decision-support tool for evaluating oral mucosal lesions. Full article

Rapid innovation-driven transitions have, across successive eras, been associated with increased psychological distress, substance use, and suicide when social and economic change outpaces the adaptive capacity of individuals, communities, institutions, and governments. The early artificial intelligence (AI) economy is emerging at an unprecedented pace, reshaping education, employment, and social organization in ways that may intensify these risks, particularly among young people. This perspective argues that school-based mental health nursing, working collaboratively with school psychologists, counsellors, physicians, and other interdisciplinary professionals, represents a practical upstream response to AI-era disruption. Drawing on historical evidence from the Industrial Revolution, population mental health research, and the contemporary mental health nursing literature, the paper examines the mechanisms through which rapid socio-economic change may contribute to psychological distress and substance-use problems. It further distinguishes AI as both a source of socio-economic disruption and a clinical tool that can support—but not replace—relationship-based nursing practice. Strengthening school-based mental health nursing within interdisciplinary systems offers a practical, prevention-oriented strategy to promote resilience, facilitate early intervention, and mitigate downstream mental health and substance-use harms during the AI transition. Full article