Search Results (1,040)

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

Cognitive load theory-informed curriculum design in health sciences education refers to the purposeful organisation of teaching strategies and learning materials based on the principles of Cognitive Load Theory (CLT), a framework developed by John Sweller in the late 1980s. CLT is grounded in cognitive psychology and recognises that the working memory has a limited capacity for processing new information. It identifies three types of cognitive load: intrinsic load, which refers to the inherent complexity of the material being learned; extraneous load, which results from ineffective instructional design or irrelevant information; and germane load, which reflects the mental effort directed toward understanding, integrating, and organising information into long-term memory. In health sciences education, students frequently engage with tasks that require the simultaneous processing of multiple interacting elements, placing high demands on working memory at specific points in time. This includes foundational biomedical sciences such as anatomy, physiology, and pathophysiology extending to applied clinical skills, diagnostic reasoning under uncertainty, health service management within complex systems, and ethically grounded decision-making. Without thoughtful instructional design, learners may be overwhelmed by excessive information and cognitive demands, which can hinder understanding, retention, and performance. Applying CLT-informed strategies, educators can reduce unnecessary cognitive burden, sequence learning activities to align with learners’ cognitive capacity, and promote deeper learning. This approach supports more effective knowledge acquisition and transfer and is particularly valuable in content dense academic environments such as medicine, nursing, allied health education, public health and health service management education. Therefore, integrating CLT-informed principles into curriculum design can help optimise learning experiences and support the development of competent health professionals. Full article

Veterinary students’ perceptions of animal welfare are shaped by both educational exposure and individual background, with direct implications for future clinical decision-making. This study evaluated 157 ninth-semester veterinary students enrolled in a Deontology and Veterinary Legal course at the University of Las Palmas de Gran Canaria (Spain) across six academic years (2019/2020–2025/2026), excluding 2020/2021 due to pandemic-related teaching modifications. Participants completed a structured, previously published questionnaire assessing twelve common dog welfare issues on a 5-point Likert scale, subsequently grouped into five dog welfare dimensions. Students consistently prioritized overt and severe dog welfare concerns, including abuse or active cruelty, lack of treatment to prevent suffering, and malnutrition, while assigning lower importance to breed-related conditions and behavioral problems. Significant differences across academic years were identified for seven items and all dimensions, revealing temporal variability in dog welfare perception between cohorts. Gender and student background also influenced responses, with female and exchange students generally assigning higher scores to selected issues. These findings suggest that the perception of dog welfare is not static but varies between cohorts across academic years and is shaped by sociocultural factors. Strengthening veterinary curricula to address less visible and socially normalized dog welfare problems may be critical to ensuring comprehensive and ethically grounded professional practice. Full article

Artificial Intelligence systems increasingly shape environmental decision making, infrastructure planning, and resource use across public and urban domains. However, prevailing AI trust and governance mechanisms, including labels, certifications, and assurance schemes, remain primarily focused on ethical and legal accountability, with limited operational attention to environmental sustainability. This paper reconceptualises AI trust mechanisms as socio-technical governance infrastructures that can support both ethical assurance and environmental accountability. Drawing on a comparative qualitative analysis of nine AI trust initiatives, the study develops a three-dimensional analytical framework embedding Environmental Performance Indicators across three governance dimensions: trust-building effectiveness, governance readiness, and sustainable adoption. Applying a systems governance lens, the framework examines how governance instruments structure information flows, institutional practices, and lifecycle feedback relevant to environmental performance. It is analytically illustrated through two urban mobility cases, Helsinki’s Whim application and Barcelona’s smart mobility system, to examine how governance conditions enable or constrain the integration of Environmental Performance Indicators in practice. Findings show that current trust mechanisms lack measurable and publicly visible environmental criteria, indicating a gap between AI assurance and environmental governance. The study contributes a systems-oriented framework for evaluating AI trust mechanisms as governance instruments capable of supporting environmental accountability. While exploratory and based on secondary data, the results indicate that future AI trust mechanisms must incorporate measurable sustainability indicators to support eco-efficient and accountable digital transformation. Full article

Background/Objectives: The rapid expansion of New Approach Methodologies (NAMs) is transforming oral biopharmaceutics by offering mechanistically rich, human-relevant tools that can reduce reliance on animal testing while improving translational confidence. Regulatory agencies, including the Food and Drug Administration (FDA) and the European Medicines Agency (EMA), are increasingly open to NAM-generated evidence, provided that methods are fit-for-purpose and scientifically justified. This review synthesizes current advances and evaluates how NAMs can be integrated across drug-development stages to enhance the prediction of oral absorption, formulation performance, and regulatory decision-making. Methods: A comprehensive literature review was conducted across classical and emerging methodologies, including in vitro permeability and solubility models, organoids, organ-on-a-chip (OoC) systems, machine learning frameworks, and mechanistic approaches such as the physiologically based pharmacokinetic (PBPK) and biopharmaceutics (PBBM) models. Emphasis was placed on physiological relevance, predictive performance, validation status, and regulatory applicability. Results: Classical tools remain essential for the Biopharmaceutics Classification System (BCS)-based biowaivers and risk-based assessments, yet they often lack physiological fidelity. NAMs provide enhanced representation of intestinal architecture, hydrodynamics, transporter activity, and metabolism. Organoids and microphysiological systems generate high-quality permeability and metabolic data, while computational NAMs enable scalable prediction of ADME properties and formulation behavior. When integrated into PBPK/PBBM models, these methods have great potential in predicting in vivo performance in humans. Evidence demonstrates that NAMs can refine, reduce, and, in specific contexts, replace animal studies without compromising scientific rigor. Conclusions: NAMs complement, rather than displace, classical biopharmaceutic tools, enabling a more mechanistic, human-centered, and ethically responsible framework for drug development. Their effective implementation will depend on continued validation, standardization, and regulatory harmonization as the field transitions toward fully NAM-supported biopharmaceutical assessment. Full article

Background/Objectives: Artificial Nutrition and Hydration (ANH) at the end of life remains a clinically and ethically complex intervention. Although international guidelines exist, data regarding the awareness of them and their perceived applicability across different population groups remain limited. This study aimed to evaluate and compare perceptions and attitudes regarding ANH among healthcare professionals, medical students, and lay respondents. Methods: A cross-sectional, questionnaire-based comparative survey was conducted between July 2025 and March 2026, including 470 respondents (338 healthcare professionals, 46 medical students, and 86 lay respondents). The survey assessed perceptions of ANH, factors influencing decision-making, and familiarity with clinical guidelines and legislation. Results: General perceptions regarding ANH were broadly similar across groups. Significant differences were observed for the importance assigned to estimated life expectancy (p < 0.001) and family opinion (p = 0.017). Associations were identified between study group and opinions on clinical guidelines (χ²(6) = 16.366, p = 0.012) and legislation (χ²(6) = 14.712, p = 0.023), with lack of knowledge more frequent among lay respondents and students. Within healthcare professionals, physicians and nurses showed significantly different responses regarding guidelines (p < 0.001). Conclusions: In this cross-sectional survey, perceptions of ANH at the end of life were largely shared, but differed in relation to prognostic factors, family involvement, and awareness of guidelines and legislation, suggesting the presence of relevant knowledge gaps in end-of-life decision-making. Full article

In this study, a hybrid multi-criteria decision-making (MCDM) model was developed for analyzing consumer adoption decisions regarding generative artificial intelligence (Gen AI). By extending the technology acceptance model (TAM) into a structured decision system, the proposed framework integrates ethical and risk-related criteria, including perceived cost, perceived risk, transparency, accountability, intellectual property concerns, and data privacy, into a formal causal and evaluative structure. First, a Delphi-based consensus process is employed to identify and refine key adoption criteria. Subsequently, the decision-making trial and evaluation laboratory (DEMATEL) method is applied to quantify causal relationships among these criteria and to construct an influence network revealing prominence and directional effects. In total, 251 questionnaires were distributed in Taiwan, and 231 valid responses were collected. The results indicated the decision-making factors that underlie the adoption of Gen AI by consumers. The results highlighted transparency as a dominant causal factor that significantly influences multiple ethical and functional dimensions of Gen AI adoption. To address uncertainty and vagueness in human judgment, fuzzy importance–performance analysis was also incorporated. Best non-fuzzy performance values were obtained through defuzzification, enabling the classification and prioritization of critical adoption factors within a four-quadrant decision matrix. The proposed framework provides a mathematically grounded decision-support model for elucidating the structural interdependencies among adoption criteria and to facilitate strategic decision making for Gen AI system design and governance. This study contributes to the MCDM and operations research literature by transforming a behavioral acceptance model into a formal decision-analytic framework, thereby enhancing the analytical rigor and applicability of TAM-based adoption studies in complex socio-technical systems. Full article

Background/Objectives: As artificial intelligence systems become increasingly embedded in everyday cognitive tasks, human–AI interaction is no longer limited to tool use but evolves into a dynamic process of mutual adaptation. While extensive research has examined algorithmic learning, far less attention has been given to how users progressively adapt to AI systems. This paper introduces the concept of algorithmic habituation, defined as the gradual accommodation of users to the regularities and predictive patterns of AI systems. The objective is to provide a neurocognitive and systems-based framework that explains this phenomenon. Methods: The study develops a conceptual and integrative framework grounded in classical theories of habituation, neuroplasticity, predictive processing, and systems theory. Building on these foundations, we propose a mechanistic model of human–AI co-adaptation, conceptualized as a recursive feedback loop involving repeated interaction, pattern recognition, expectation stabilization, and cognitive economy. In addition, a typology of algorithmic habituation is advanced, alongside proposed empirical pathways for future validation, including scale development, experimental paradigms, and longitudinal designs. Results: The proposed framework suggests that repeated interaction with AI systems leads to stabilization of cognitive expectations, reduced cognitive effort, and increased behavioral standardization. This process extends beyond perceptual habituation into higher-order domains, including decision-making, creativity, and moral judgment. The typology identifies four primary forms of algorithmic habituation: cognitive, decisional, creative, and moral. The model predicts both adaptive outcomes (efficiency, reduced cognitive load) and maladaptive consequences (reduced reflexivity, automation bias, and potential erosion of critical thinking). Conclusions: Algorithmic habituation represents a novel construct at the intersection of neuroscience, cognitive psychology, and human–AI interaction. By framing user adaptation as a form of neurocognitively grounded habituation within recursive systems, this paper contributes a new perspective to understanding AI integration in human cognition. The framework has implications for digital wellbeing, education, and AI ethics, and opens multiple avenues for empirical research. Full article

This systematic review synthesises the qualitative literature on Māori leadership to examine how leadership is conceptualised, enacted, and constrained, and what this implies for Aotearoa New Zealand’s health system. Across included studies, Māori leadership is grounded in whakapapa-based legitimacy, tikanga and mātauranga Māori, and collective responsibility for relational, cultural, and intergenerational wellbeing; these foundations persist across “traditional” and “contemporary” settings, with differences reflecting institutional conditions rather than shifts in core values. Interpreting the literature through a Māori cultural lens, the review shows that leadership is often exercised within Crown-dominated organisations where Māori authority is not the default, requiring leaders to navigate multiple accountabilities to iwi and communities, organisational mandates, and statutory obligations. Hybridity emerges as a structurally produced feature of practice, integrating Māori relational ethics with bureaucratic, professional, and governance requirements and ongoing translation work to make Māori priorities legible within institutional systems. Health-sector evidence illustrates how commissioning, funding, and accountability arrangements can limit Māori decision-making, increase leadership burden, and constrain sustainability and leadership pipelines. The review concludes that strengthening Māori leadership in health requires organisational and system change—such as clearer Māori decision rights, resourced Māori-led priority setting, and accountability mechanisms that operationalise equity and anti-racism—alongside targeted research on governance, commissioning, and system design. Full article

Deep learning (DL) has transformed cancer imaging by enabling automated tumour detection, classification, and risk prediction. Despite impressive diagnostic performance, limited explainability and poor uncertainty calibration continue to restrict clinical integration. This review is guided by five research questions that examine the challenges, impact, and translational implications of explainable artificial intelligence (XAI) in oncology imaging. We identify key barriers to trust, including dataset bias, shortcut learning, opacity of convolutional neural networks, and workflow misalignment. Evidence suggests that explainable models can increase clinician confidence, reduce false positives, and improve collaborative decision-making when explanations are faithful, semantically meaningful, and uncertainty aware. We evaluate architectural strategies that embed interpretability such as concept-bottleneck models, prototype-based learning, and attention regularization along with post hoc techniques. Beyond performance metrics, we examine how interpretable AI aligns with clinical reasoning processes and analyse regulatory, ethical, and medico-legal considerations influencing deployment. The findings indicate that explainability alone is insufficient, durable trust requires epistemic alignment, prospective validation, lifecycle governance, and equity-focused evaluation. By reframing explainability as a structural design principle rather than a supplementary feature, this review outlines a pathway toward accountable and clinically dependable AI systems in oncology. Full article

Doping remains a major threat to athlete health and sport integrity. Although anti-doping efforts have traditionally focused on athletes, increasing attention has turned to Athlete Support Personnel (ASP) due to their influence on athletes’ decisions, behaviors and involvement in anti-doping rule violations. This narrative review aimed to synthesize the existing literature on the role of ASP (including coaches, physicians, pharmacists, sport psychologists, nutritionists, physiotherapists, parents and other family members) in anti-doping, with particular attention to their influence on athletes’ knowledge, attitudes, behaviors, education and decision-making related to doping. Coaches, physicians, and pharmacists are among the ASP groups most frequently examined in the literature, although substantial knowledge gaps remain across all groups. Coaches shape motivational climates and ethical norms but often lack adequate understanding of anti-doping regulations and supplement risks. Physicians and pharmacists play key roles in medication management and Therapeutic Use Exemptions procedures, though incomplete regulatory knowledge may contribute to inadvertent violations. Nutritionists are central in preventing supplement-related doping, while research on sport psychologists and physiotherapists remains limited despite their preventive potential. Parents significantly shape athletes’ moral development and susceptibility to doping, acting as protective or risk factors depending on family dynamics. Overall, anti-doping education for ASP remains inconsistent. In conclusion, ASP plays an essential yet heterogeneous role in influencing doping-related behaviors. Strengthening role-specific and interdisciplinary anti-doping education, particularly within university programs and professional development, appears critical for enhancing ASP competence and promoting a sustainable culture of clean sport. Full article

The Camino de Santiago, a UNESCO-listed pilgrimage route, has experienced sustained growth in visitor numbers, challenging municipalities to preserve cultural integrity while ensuring service quality. This study reviews people-counting technologies and proposes a smart pilgrim management framework grounded in flux measurement systems to support data-driven and sustainable decision-making. Drawing on the smart tourism literature, the conceptual framework integrates infrared counters, mobile tracking solutions, and GPS/Wi-Fi data to generate real-time insights into pilgrim flows. A pilot simulation illustrates how these data can inform operational and strategic planning. The framework enables local authorities to monitor pedestrian movements, anticipate service demands (sanitation, accommodation, and safety), and detect overcrowding in sensitive heritage areas. By incorporating technological solutions into traditionally low-tech pilgrimage settings, municipalities can transition from reactive to proactive management approaches. The paper contributes a scalable and ethically grounded framework tailored to heritage pilgrimage routes, advancing smart tourism applications in culturally significant contexts. Full article

In ethics-sensitive product development, Generative AI can improve the efficiency of concept generation, but it also raises challenges related to accountability, value alignment, and decision transparency. To address limitations in current human-AI co-design processes, including unclear allocation of decision-making authority, insufficiently structured translation from design requirements to design constraints, and limited explainability in scheme evaluation, this study proposes an explainable Human–Computer Interaction (HCI)-based decision support framework for human-AI co-design, termed GAGT. The framework integrates Generative AI with multi-criteria decision-making methods. Specifically, the Analytic Hierarchy Process (AHP) is used to structure design requirements and determine their priorities, Grey Relational Analysis (GRA) is used to compare candidate schemes, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to support transparent final ranking. Within the framework, human designers are mainly responsible for requirement confirmation, priority judgment, review at key checkpoints, and final scheme selection, while AI mainly supports information organization, candidate scheme generation, and quantitative comparison. The framework was applied to the design of a community medical vehicle through a small-sample, case-based, quasi-experimental study. Compared with the human-only condition, the GAGT-supported condition reduced design time by 56.1%. Compared with the AI-autonomous condition, it showed no observed HIPAA violations and a Value Drift Index of 16.1%, indicating better consistency with human-defined priorities. The results suggest that the proposed framework may improve design efficiency while supporting clearer human oversight and decision explainability in Generative AI-assisted design, and may provide a structured approach to organizing human and AI roles in ethics-sensitive design tasks. Full article

As artificial intelligence (AI) becomes increasingly embedded in organizational processes, questions about its implications for leadership have gained growing relevance. However, the existing literature remains fragmented, often addressing strategy, leadership capabilities, governance structures, or ethical concerns in isolation, without explaining how these dimensions interact to shape leadership effectiveness in AI-driven environments. This study conducts a PRISMA-guided systematic review of 33 peer-reviewed articles to examine how AI-embedded leadership is conceptualized across contexts. By synthesizing findings across strategic, human, and governance domains, the analysis identifies recurring patterns and structural relationships in the literature. The results indicate that effective leadership in AI-intensive settings is not determined solely by technological adoption or digital competencies, but by the alignment between the depth of AI integration in decision-making processes, leaders’ capacity to interpret and oversee algorithmic outputs, and the presence of governance mechanisms that ensure transparency, accountability, and trust. While some studies highlight potential opportunities associated with AI, these remain less systematically developed compared to the extensive focus on challenges and emerging risks. On this basis, the study introduces the AI-Leadership Configurational Framework (ALCF), a multi-level model that conceptualizes leadership effectiveness as the outcome of systemic alignment. The framework integrates previously disconnected debates and provides a coherent foundation for future empirical research on leadership in the algorithmic age. Full article

Rapid urbanization and the growth of data-driven planning have increased the need for tools that support integrated, transparent, and accountable urban governance. While Building Information Modeling (BIM) is well established in project delivery, its potential role in city-scale governance remains underexplored. This study conducts a structured qualitative evidence synthesis informed by PRISMA reporting principles and comparative case analysis to investigate how BIM, in combination with GIS, IoT, and AI, intersects with emerging digital governance practices. Through a synthesis of peer-reviewed research and documented case studies, the review evaluates how BIM supports data integration, interoperability, decision-making, regulatory compliance, collaborative governance, and sustainability. The findings suggest that BIM functions as a governance-support infrastructure when embedded within coordinated institutional frameworks, standardized data environments, and interoperable digital ecosystems. Based on these insights, the paper proposes a conceptual framework that organizes BIM governance into technical, institutional, social, and ethical–regulatory dimensions. The review suggests that BIM’s governance potential depends on institutional alignment, regulatory clarity, and sustained organizational capacity, rather than technological capability alone. Full article