Search Results (5,233)

Background. Artificial intelligence (AI) is increasingly recognized as a transformative technology in healthcare, with growing interest in its applications within pediatric dentistry. Given the unique clinical, developmental, and behavioral characteristics of pediatric patients, AI-based systems may offer valuable support in improving diagnosis, prevention, and treatment planning. Methods. A narrative review was conducted to synthesize current evidence on AI applications in pediatric dentistry. A comprehensive search strategy, including predefined keywords and free terms, was applied across multiple databases (Embase, Scopus, PubMed, and Web of Science) up to 1 January 2026. Reviews addressing AI-based technologies in pediatric dental care were selected and analyzed. Results. The available literature indicates that AI is being progressively applied across multiple domains of pediatric dentistry, although with varying levels of evidence. More extensively investigated areas include diagnostic imaging, caries detection, orthodontic assessment, and growth evaluation, where AI systems—particularly those based on machine learning and deep learning—have demonstrated high accuracy and reproducibility. Other emerging fields, such as remote monitoring, behavioral management, preventive strategies, and patient education, show promising potential but remain less explored. Overall, AI-based tools appear to enhance diagnostic support, enable early detection of oral conditions, and contribute to more personalized and efficient clinical workflows. Conclusions. AI represents a rapidly evolving adjunct in pediatric dentistry with the potential to improve clinical decision-making, preventive care, and patient management. Despite encouraging results, further validation in real-world settings, along with careful consideration of ethical, legal, and data-related challenges, is required to support its responsible integration into routine clinical practice. Full article

Artificial intelligence (AI) is increasingly embedded in students’ learning practices, yet little is known about how AI engagement evolves from an external technological aid into an agentic component of self-regulated learning. This study applies psychological network analysis to examine the structural relations among students’ knowledge of AI, perceived value and perceived cost of AI, intention to use AI, and three core self-regulated learning processes—forethought, performance control, and self-reflection—across different levels of AI use frequency. The study was conducted on a sample of 673 university students and early-career graduates. Networks were estimated using EBICglasso for the full sample and separately for low-, moderate-, and high-frequency AI users. Across all models, a stable two-system organization emerged, consisting of an AI appraisal subsystem (knowledge, value, cost, intention) and a self-regulation subsystem (forethought, performance control, self-reflection). However, the connectivity between these subsystems differed systematically by usage frequency. Among low-frequency users, perceived cost was more prominently positioned within the appraisal subsystem, suggesting that cost-related concerns may be more salient in lower-frequency use contexts. In contrast, in the moderate- and high-frequency groups, performance control appeared more centrally positioned at the interface between appraisal and self-regulation, suggesting stronger alignment between AI-related appraisals and performance-level regulatory processes in these groups. Students’ knowledge of AI displayed context-dependent structural roles across networks, consistent with a variable relational position across use-frequency groups. Overall, the findings suggest that AI appraisal and self-regulated learning form partially distinct but interconnected subsystems, and that their configuration may vary across AI use-frequency groups. Because subgroup comparisons were descriptive and formal stability analyses were not conducted, these findings should be interpreted as exploratory. The results do not support causal or developmental inference and require replication using bootstrapped stability analyses and formal network comparison procedures. Full article

Across modern Asia and many other regions, artificial intelligence is transforming religious life in diverse and profound ways. Robot priests chant sutras at Japanese Buddhist temples, AI-powered apps offer personalised coaching in Quranic recitation to millions of Muslims, and bereaved families consult algorithm-generated avatars of the deceased in China. They are neither merely tools for instrumental use nor channels for transmitting pre-existing religious authority. Instead, they create new forms of religious content, new types of spiritual encounters for religious users, and new structures of authority. This paper argues that understanding these phenomena requires theoretical innovation beyond simply applying existing concepts to new domains. Drawing on Actor–Network Theory, algorithmic culture studies, and scholarship on Asian religious traditions, the paper proposes the theoretical framework of generative charisma, theorising how AI systems gain religious authority through three interconnected mechanisms: captivation by generation, intimacy trust through personalisation, and oscillating enchantment. It also highlights accountability as a structural issue that needs critical discussion regarding governance. The paper demonstrates the framework’s usefulness by examining AI recitation coaching in Islamic practice and AI grief avatars in Chinese Buddhist mourning, showing its relevance across different religious traditions and technological forms. Full article

With the rapid development of artificial intelligence (AI), its implications for income distribution have attracted increasing attention. As a key indicator of earnings differences between high- and low-skilled workers, the skill premium is important for distributional equity and sustainable economic and social development. Using AI-related patent data from Chinese listed firms, this paper constructs a firm-level measure of AI development and examines its impact on the skill premium within firms. The results show that AI development significantly increases the firm-level skill premium. Mechanism analysis suggests that AI increases the firm-level skill premium by substituting for low-skilled labor, improving firm productivity, promoting capital deepening, and facilitating technological upgrading. The main findings remain robust after addressing endogeneity using an instrumental variable approach and conducting a series of robustness checks, including alternative constructions and measures of the dependent variable, alternative measures of AI development, AI pilot zone policy shock tests, and alternative sample restrictions. Heterogeneity analysis further shows that the effect is more pronounced in non-state-owned firms, firms with higher levels of digitalization, and firms operating in industries with lower market concentration. Further analysis indicates that AI development may also reduce firms’ labor income share and widen income disparities across industries. These findings highlight the need to strengthen workers’ skills and adaptability, improve income distribution mechanisms, and promote a more balanced relationship between technological progress and social equity. Full article

The recirculating aquaculture system (RAS) marks a significant shift in global aquaculture, transitioning to controlled, land-based production. This review highlights technological advancements that enable the treatment and reuse of over 90% of water, thereby enhancing water quality and production efficiency. These features position RAS as a cornerstone of sustainable seafood production. This review introduces the RAS Readiness Level (RRL) framework which is a novel, structured approach to assess the commercial maturity of emerging RAS technologies. Applying the RRL to six key technological domains (from digital AI systems to biological PHB recovery) reveals a pervasive pilot-scale purgatory where most innovations stagnate at RRL 4–6. It further addresses advanced processes such as membrane bioreactors, denitrification reactors, and the conversion of waste into valuable products. Furthermore, this review addresses persistent challenges, including high energy demand, economic viability, and the accumulation of pathogens. Finally, it focuses on the emergent integration of the Internet of Things (IoT) and artificial intelligence (AI), which are revolutionizing RAS management through data-driven optimization. By synthesizing current innovations, this review envisions a future of intelligent, closed-loop RAS where advanced IoT- and AI-driven technologies optimize water quality and feeding strategies to minimize ecological impact while enhancing sustainability and productivity. Full article

Learning is fundamentally human, even as Artificial Intelligence (AI) challenges human exclusivity. AI, along with Virtual Reality (VR), emerges as a powerful tool that is set to transform higher education, the institutional embodiment of this pursuit at its highest level. These technologies offer the potential not to replace the human factor, but to enhance our ability to create more adaptive, immersive, and truly human-centric learning experiences, aligning powerfully with the emerging vision of Education 5.0, which emphasizes ethical, collaborative learning ecosystems. This research maps how AI and VR tools act as a disruptive force, examining additionally their capabilities and limitations. Moreover, it explores how AI and VR interact to overcome traditional pedagogy’s constraints, fostering environments where technology serves human learning goals. Employing a comprehensive two-month audit of over 60 AI, VR, and AI-VR hybrid tools, the study assesses their functionalities and properties such as technical complexity, cost structures, integration capabilities, and compliance with ethical standards. Findings reveal that AI and VR systems provide significant opportunities for the future of education by providing personalized and captivating environments that encourage experiential learning and improve student motivation across disciplines. Nonetheless, numerous challenges limit widespread adoption, such as advanced infrastructure requirements and strategic planning. By articulating a structured evaluative framework and highlighting emerging trends, this paper provides practical guidance for educational stakeholders seeking to select and implement AI and VR tools in higher education. Full article

Thoracic malignancies are uniquely positioned for the integration of emerging technologies such as artificial intelligence (AI), which have the potential to advance precision oncology across the cancer care continuum. In cancer screening, AI has emerged as a promising strategy to enhance diagnostic accuracy, efficiency, and scalability. Deep learning applied to pathology (pathomics) and imaging (radiomics) has enabled the development of novel, noninvasive tools capable of predicting histologic and molecular features that may correlate with treatment response or toxicity. In drug discovery, computational approaches can analyze large-scale genomic, chemical, and clinical datasets to accelerate target identification and match candidate compounds to available targets; this may be particularly useful in the context of resistance to targeted therapy. AI tools may also support treatment planning for radiation and surgery, guide systemic therapy selection, and facilitate continuous monitoring for early identification of treatment resistance or toxicity. As these technologies are integrated into clinical workflows, careful attention to ethical, regulatory, and clinical governance frameworks will be essential to ensure equitable implementation and bias mitigation. Maintaining human oversight and a human-centered approach remain critical, as complex treatment decisions and sensitive patient interactions are central to the care of patients with thoracic malignancies. Full article

Artificial Intelligence (AI) has traditionally been developed according to a model-centric paradigm, in which progress is driven by increasingly sophisticated learning architectures applied to largely fixed datasets. However, this paradigm exhibits well-known limitations, including sensitivity to label noise, distribution shifts, adversarial perturbations, and limited transparency and reproducibility. These issues indicate that many of the current bottlenecks of AI systems arise from deficiencies in data rather than from model design. In this paper, we adopt and formalize the Data-Centric Artificial Intelligence (DCAI) paradigm, which places data quality, semantic consistency, and representativeness at the core of the AI lifecycle. From this perspective, performance, robustness, interpretability, and regulatory compliance are primarily achieved through systematic data engineering, including data curation, enrichment, validation, and continuous monitoring, rather than through repeated model re-engineering. The contributions of this work are threefold. First, a conceptual framework is provided to clarify the epistemic and methodological foundations of DCAI and distinguish it from traditional model-centric approaches. Second, a data-centric lifecycle is presented, covering training data development, inference data design, and data maintenance and integrating techniques such as semantic data representation, active learning, synthetic data generation, and drift-aware quality control. Third, the role of DCAI in the context of Generative AI is analyzed, showing how data-centric practices are essential to ensure robustness, accountability, and responsible deployment of large-scale generative models. Overall, this work positions DCAI as a coherent methodological and technological framework for the development of trustworthy, resilient, and sustainable AI systems, making a research contribution and providing a reference model for industrial and regulatory contexts. Full article

Background/Objectives: Artificial intelligence (AI) shows promise in supporting mammography interpretation and triaging referrals, potentially enhancing breast screening. However, successful AI integration depends on patient acceptance and trust. This study explores patient and public perceptions of AI in breast imaging and clinical decision-making to identify knowledge gaps and guide communication strategies. Methods: Paper surveys were distributed to women attending the Breast Care Unit at Queen’s Hospital, Burton, and the London Breast Institute between August and December 2025. Demographic data, levels of trust and comfort with AI, and concerns about AI were collected. Responses were analysed using descriptive statistics, Pearson’s Chi-square tests with Cramér’s V and thematic analysis. Results: One hundred and twenty participants completed the survey. Fifty percent would accept AI alongside clinicians for interpretation of mammograms or ultrasound scans, significantly associated with no previous breast cancer diagnosis (p = 0.02; Cramér’s V = 0.22, 2 degrees of freedom (df)) and technological comfort (p < 0.001; Cramér’s V = 0.42, 1 df). Lower acceptance was found among those with prior diagnosis and low comfort with technology. Acceptance of AI-assisted triage (44.5%) was also significantly associated with technological comfort (p = 0.008; Cramér’s V = 0.30, 1 df). Eighty percent reported no knowledge of AI use in breast clinics, and only 37% would trust AI findings. Qualitative analysis identified three themes: (1) clinician oversight as indispensable, (2) the knowledge gap as a barrier to acceptance, and (3) concerns about operational risks and accountability. Conclusions: Although patients were generally receptive to AI, acceptance was conditional on clinician supervision. Limited awareness and concerns about diagnostic accuracy remain barriers to implementation. Educational initiatives should precede widespread adoption to support informed and confident patient acceptance of AI-assisted imaging and decision-making. Full article

Background/Objectives: Artificial intelligence (AI) models are increasingly applied across the assisted reproductive technology (ART) workflow, including male-factor assessment, ovarian stimulation, endometrial receptivity evaluation, embryo selection and prediction of pregnancy outcomes. However, many systems remain difficult to interpret, raising concerns regarding transparency, clinical integration and patient communication. Explainable artificial intelligence (XAI) aims to address these limitations by making model behavior more accessible to clinicians and embryologists. This review aimed to provide a narrative, concept-driven synthesis of how XAI has been implemented in ART, to critically examine methodological quality and clinical relevance and to outline priorities for responsible translation into practice. Methods: A structured narrative review was conducted using PubMed/MEDLINE as the primary database, supplemented by targeted reference-list screening of key primary studies and recent cross-disciplinary reviews relevant to AI in ART. Studies were curated and classified according to stage of the ART workflow, data modality, model family, explanation technique and validation strategy. Methodological features, performance reporting and implementation considerations were qualitatively appraised. Results: Most XAI applications in ART fall into two dominant categories: (i) feature-attribution methods such as SHAP and LIME applied to tabular clinical and laboratory data and (ii) saliency-based approaches, including Grad-CAM and related techniques, applied to embryo and ultrasound imaging. These methods can improve transparency and support counselling by clarifying which variables or image regions influence predictions. However, the majority of studies are retrospective and single centre, with limited external validation and heterogeneous outcome definitions, often prioritising clinical pregnancy over live birth. Calibration, decision-analytic evaluation and prospective assessment remain uncommon. XAI outputs are frequently interpreted as biologically causal despite being derived from observational data, highlighting the need for cautious clinical framing. Conclusions: XAI in ART has progressed from proof-of-concept demonstrations to early clinically oriented tools, but robust validation, standardised reporting and thoughtful workflow integration are still needed. Explanations can enhance auditability and communication, yet they do not compensate for methodological weakness. Future progress will depend on higher-quality multi-centre data, evaluation beyond discrimination metrics and governance frameworks that ensure transparency, fairness and sustained performance in real-world practice. Full article

Artificial intelligence (AI) is transforming modern agriculture from experience-driven practices to data-driven production paradigms. To provide an in-depth analysis of AI technologies in intelligent agriculture, we retrieved literature from Web of Science, IEEE Xplore, Google Scholar and Scopus, covering publications from 2015 to 2025, and 85 articles remained after screening 1867 relevant publications. These articles are grouped into three stages from perception, to decision making, to execution (PDE) in a closed-loop framework. At the perception level, we highlight progress in intelligent sensing systems, such as unmanned aerial vehicle (UAV) and multi-modal monitoring platforms, for crop disease and pest detection, growth monitoring and abiotic stress assessment. At the decision making level, integration of heterogeneous data sources, including meteorological records, soil measurements, remote sensing (RS) imagery and market information, supports advanced analytics, such as yield prediction, pest and disease warning, irrigation and fertilization planning, and crop management optimization. At the execution level, agricultural robots equipped with simultaneous localization and mapping (SLAM) and deep reinforcement learning (RL) facilitate precision spraying, autonomous harvesting, and unmanned field operations. Overall, AI technologies demonstrate substantial potential in the PDE pipeline of agricultural production. However, several challenges remain, including heterogeneous data fusion, limited generalization across diverse environments, complex system integration, and high hardware and deployment costs. Future directions are discussed from the perspectives of lightweight model design, cross-platform standardization, enhanced human–machine collaboration, and a deeper integration of emerging AI paradigms to support scalable, robust, and autonomous agricultural intelligence systems. Full article

Extreme meteorological phenomena—characterized by gale-force winds, torrential rainfall, and ice-snow accumulation—pose significant threats to the operational safety of high-speed railways, with wind-induced hazards being especially critical. Such events can trigger catastrophic incidents, including train derailments and service disruptions, as evidenced by numerous documented cases worldwide. To bolster the wind resilience of high-speed railway systems, high-precision wind speed prediction has become a cornerstone for ensuring operational safety. This research presents a systematic review of international advancements in railway wind early warning systems, critically evaluating the technical attributes and performance constraints of four primary paradigms: physical numerical models, statistical methods, machine learning algorithms, and hybrid frameworks. Moving beyond a simple taxonomy, this paper delineates the strengths, limitations, and domain-specific applicability of each approach within the high-speed railways context. Furthermore, it assesses the transformative potential of emerging large-scale Artificial Intelligence (AI) meteorological models for wind speed forecasting. A quantitative comparison is provided to facilitate rigorous methodological assessment. The findings reveal four critical technical bottlenecks: (1) low computational efficiency of numerical models; (2) insufficient spatiotemporal resolution of monitoring data; (3) poor generalization of predictive models; and (4) the “black-box” nature and weak interpretability of AI models. To address these, this paper posits that future research should prioritize key technologies including multi-source heterogeneous data fusion, algorithmic optimization, design of intelligent algorithms, probabilistic risk forecasting, and the synergistic integration of AI with numerical weather prediction (NWP). Such advancements will catalyze the development of more robust HSR wind warning systems, ensuring sustained safety and operational efficiency under volatile meteorological conditions. Full article

Academic stress is prevalent among university students and affects their evaluation of educational environment quality, fairness, and supportiveness. Based on the challenge–hindrance stressor framework and transactional stress-coping model, this study explores how challenge and hindrance stressors (HSs) shape perceived decent education (DE), focusing on the mediating role of artificial intelligence use (AIUSE) and moderating effect of interpersonal interaction (II). Using partial least squares structural equation modeling (PLS-SEM) to analyze survey data from 520 university students, the results show that both stressors positively predict AIUSE, which in turn improves perceived DE and mediates the stressor-DE relationship. II negatively moderates the AIUSE–DE link: the positive effect weakens as II increases. Moderated mediation analysis indicates that the indirect effects via AIUSE are only significant at low II levels. These findings highlight AI-enabled learning as an adaptive coping strategy and the necessity of integrating technological and interpersonal resources to enhance student well-being in higher education. Full article

Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD), asthma, pulmonary fibrosis, and acute respiratory infections remain a major global health challenge due to their complex pathophysiology and limited therapeutic options. Conventional 2D cultures and animal models have provided foundational insights; however, they often fail to accurately replicate the human lung’s intricate architecture, immune interactions, and patient-specific variability. Recent advances in vitro technologies have transformed pulmonary research, enabling the generation of physiologically relevant and translational disease models. The review highlights the progression of lung research platforms from traditional monolayer cultures to advanced systems such as air–liquid interface models and 3D lung organoids. These cutting-edge models more effectively mimic the biochemical, mechanical, and spatial microenvironment of the respiratory system, enhancing the fidelity of disease modelling and drug screening. In parallel, the integration of computational modelling and artificial intelligence (AI) has emerged as a powerful synergistic approach. AI-driven analytics facilitate high-throughput imaging, biomarker discovery, and patient-stratified therapeutic prediction, while computational tools simulate disease networks, mechanobiological interactions, and pharmacological responses. The convergence of these technologies supports a deeper understanding of pulmonary disease progression and accelerates the development of precision therapeutics. Collectively, this review underscores the transformative potential of combining in vitro lung models with advanced computational and AI methodologies. This synergy not only improves translational relevance and reduces reliance on animal testing but also paves the way for personalised interventions that better address the complexity of human pulmonary disease. Full article

Higher technical and professional education is increasingly discussed in relation to workforce readiness, innovation, and societal inclusion. In Italy, the PNRR-funded Rome Technopole operates as a multi-institutional ecosystem in which universities, research organizations, industry, and public bodies interact through a Hub & Spoke model to support training and innovation activities. Among its components, Spoke 4 addresses professional higher technical education through the co-development of modular learning initiatives involving multiple stakeholders. This commentary examines the role and activities of the Italian National Institute of Health (ISS) within this context, with particular reference to the development of two pilot modules: one on Artificial Intelligence and Algorethics, and one on Accessibility and Assistive Technologies, including applications supported by AI. The paper combines a conceptual discussion of the approach with selected empirical insights derived from pilot implementation, including stakeholder engagement processes, structured evaluations, and thematic prioritization exercises. The findings suggest the perceived relevance of multi-stakeholder co-design, the use of flexible and modular learning formats, and the integration of technical and ethical dimensions in higher technical education. At the same time, they point to challenges related to coordination, scalability, and alignment across institutional actors. Rather than proposing a definitive model, the Spoke 4 experience is discussed as a context-specific case that may offer insights contributing to ongoing debates on the design and implementation of higher technical education in complex, multi-institutional settings. Full article