Search Results (698)

Early and differential diagnosis of dementia is essential for timely and targeted care. This study investigated the feasibility of using an artificial intelligence (AI)-based system to discriminate between different stages and etiologies of dementia by analyzing facial emotions. We collected video recordings of 64 participants exposed to standardized audio-visual stimuli. Facial emotion features in terms of valence and arousal were extracted and used to train machine learning models on multiple classification tasks, including distinguishing individuals with mild cognitive impairment (MCI) and overt dementia from healthy controls (HCs) and differentiating Alzheimer’s disease (AD) from other types of cognitive impairment. Nested cross-validation was adopted to evaluate the performance of different tested models (K-Nearest Neighbors, Logistic Regression, and Support Vector Machine models) and optimize their hyperparameters. The system achieved a cross-validation accuracy of 76.0% for MCI vs. HCs, 73.6% for dementia vs. HCs, and 64.1% in the three-class classification (MCI vs. dementia vs. HCs). Among cognitively impaired individuals, a 75.4% accuracy was reached in distinguishing AD from other etiologies. These results demonstrated the potential of AI-driven facial emotion analysis as a non-invasive tool for early detection of cognitive impairment and for supporting differential diagnosis of AD in clinical settings. Full article

Background: Chronic pain affects nearly one in five adults worldwide and is increasingly recognized not only as a disease but as a potential risk factor for neurocognitive decline and dementia. While some evidence supports this association, existing systematic reviews are static and rapidly outdated, and none have leveraged advanced methods for continuous updating and robust uncertainty modeling. Objective: This protocol describes a living systematic review with dose–response Bayesian meta-analysis, enhanced by artificial intelligence (AI) tools, to synthesize and maintain up-to-date evidence on the prospective association between any type of chronic pain and subsequent cognitive decline. Methods: We will systematically search PubMed, Embase, Web of Science, and preprint servers for prospective cohort studies evaluating chronic pain as an exposure and cognitive decline as an outcome. Screening will be semi-automated using natural language processing models (ASReview), with human oversight for quality control. Bayesian hierarchical meta-analysis will estimate pooled effect sizes and accommodate between-study heterogeneity. Meta-regression will explore study-level moderators such as pain type, severity, and cognitive domain assessed. If data permit, a dose–response meta-analysis will be conducted. Living updates will occur biannually using AI-enhanced workflows, with results transparently disseminated through preprints and peer-reviewed updates. Results: This is a protocol; results will be disseminated in future reports. Conclusions: This living Bayesian systematic review aims to provide continuously updated, methodologically rigorous evidence on the link between chronic pain and cognitive decline. The approach integrates innovative AI tools and advanced meta-analytic methods, offering a template for future living evidence syntheses in neurology and pain research. Full article

With the development of generative artificial intelligence (AI) technology, collaboration between humans and AI is expected to improve productivity, efficiency, and safety in various industries. This study presents and empirically analyzes the factors affecting collaboration between humans and AI. This study presents and empirically analyzes a research model based on the antecedents of calculative-based, cognition-based, knowledge-based, and social influence-based trust. A total of 305 valid data points were collected through questionnaires completed by experts, office workers, and graduate students, and were analyzed using structural equation modeling. The analysis showed that all antecedents except familiarity, an antecedent of knowledge-based trust, significantly affected trust. Full article

Cognitive biases continue to pose significant challenges in executive decision-making, often leading to strategic inefficiencies, misallocation of resources, and flawed risk assessments. While traditional decision-making relies on intuition and experience, these methods are increasingly proving inadequate in addressing the complexity of modern business environments. Despite the growing integration of big data analytics into executive workflows, existing research lacks a comprehensive examination of how AI-driven methodologies can systematically mitigate biases while maintaining transparency and trust. This paper addresses these gaps by analyzing how big data analytics, artificial intelligence (AI), machine learning (ML), and explainable AI (XAI) contribute to reducing heuristic-driven errors in executive reasoning. Specifically, it explores the role of predictive modeling, real-time analytics, and decision intelligence systems in enhancing objectivity and decision accuracy. Furthermore, this study identifies key organizational and technical barriers—such as biases embedded in training data, model opacity, and resistance to AI adoption—that hinder the effectiveness of data-driven decision-making. By reviewing empirical findings from A/B testing, simulation experiments, and behavioral assessments, this research examines the applicability of AI-powered decision support systems in strategic management. The contributions of this paper include a detailed analysis of bias mitigation mechanisms, an evaluation of current limitations in AI-driven decision intelligence, and practical recommendations for fostering a more data-driven decision culture. By addressing these research gaps, this study advances the discourse on responsible AI adoption and provides actionable insights for organizations seeking to enhance executive decision-making through big data analytics. Full article

Advances in multimodal artificial intelligence enable new sensor-inspired approaches to lie detection by combining behavioral perception with generative reasoning. This study presents a deception detection framework that integrates deep video and audio processing with large language models guided by chain-of-thought (CoT) prompting. We interpret neural architectures such as ViViT (for video) and HuBERT (for speech) as digital behavioral sensors that extract implicit emotional and cognitive cues, including micro-expressions, vocal stress, and timing irregularities. We further incorporate a GPT-5-based prompt-level fusion approach for video–language–emotion alignment and zero-shot inference. This method jointly processes visual frames, textual transcripts, and emotion recognition outputs, enabling the system to generate interpretable deception hypotheses without any task-specific fine-tuning. Facial expressions are treated as high-resolution affective signals captured via visual sensors, while audio encodes prosodic markers of stress. Our experimental setup is based on the DOLOS dataset, which provides high-quality multimodal recordings of deceptive and truthful behavior. We also evaluate a continual learning setup that transfers emotional understanding to deception classification. Results indicate that multimodal fusion and CoT-based reasoning increase classification accuracy and interpretability. The proposed system bridges the gap between raw behavioral data and semantic inference, laying a foundation for AI-driven lie detection with interpretable sensor analogues. Full article

The hospitality sector is undergoing a rapid digital change due to smart technology and artificial intelligence. This presents both possibilities and problems for the development of sustainable innovation. Yet, little is known about how leaders’ technological competencies affect employees’ capacity to engage in environmentally responsible innovation. This study addresses this gap by examining how leaders’ competencies in smart technology, artificial intelligence, robotics, and algorithms (STARA) shape employees’ green innovative behavior in hotels. Anchored in person–job fit theory and cognitive appraisal theory, we propose that when employees perceive a strong alignment between their skills and the technological demands introduced by STARA, they are more likely to appraise such technologies as opportunities (challenge appraisals) rather than threats (hindrance appraisals). These appraisals, in turn, mediate the link between leadership and green innovation. Convenience sampling was used to gather data from staff members at five-star, ecologically certified hotels in Sharm El-Sheikh, Egypt. According to structural equation modeling using SmartPLS, employees’ green innovation behaviors are improved by leaders’ STARA abilities. Crucially, staff members who viewed STARA technologies as challenges (i.e., chances for learning and development) converted leadership skills into more robust green innovation results. Conversely, employees who perceived these technologies as obstacles, such as burdens or threats, diminished this beneficial effect and decreased their desire to participate in green innovation. These findings highlight that the way employees cognitively evaluate technological change determines whether leadership efforts foster or obstruct sustainable innovation in hotels. Full article

This study aims to explore the utility of artificial intelligence (AI) in classifying dementia severity based on gait analysis data and to examine how machine learning (ML) can address the limitations of conventional statistical approaches. The study included 34 individuals with mild cognitive impairment (MCI), 25 with mild dementia, 26 with moderate dementia, and 54 healthy controls. A support vector machine (SVM) classifier was employed to categorize dementia severity using gait parameters. As complexity and high dimensionality of gait data increase, traditional statistical methods may struggle to capture subtle patterns and interactions among variables. In contrast, ML techniques, including dimensionality reduction methods such as principal component analysis (PCA) and gradient-based feature selection, can effectively identify key gait features relevant to dementia severity classification. This study shows that ML can complement traditional statistical analyses by efficiently handling high-dimensional data and uncovering meaningful patterns that may be overlooked by conventional methods. Our findings highlight the promise of AI-based tools in advancing our understanding of gait characteristics in dementia and supporting the development of more accurate diagnostic models for complex or large datasets. Full article

Generative artificial intelligence (GenAI) holds significant potential to enhance university students’ learning. However, over-reliance on it to complete academic tasks poses a risk to academic achievement by potentially encouraging cognitive outsourcing. Despite this growing concern and an expanding body of research on GenAI usage, the mechanisms through which GenAI dependency and perceived teacher caring affect their academic achievement and self-efficacy remain underexplored. Based on the theory of media system dependence, this study explores the mechanisms through which university students’ dependency on GenAI affects their academic outcomes, focusing on the mediating role of self-efficacy and moderating role of perceived teacher caring. A survey was conducted with 418 university students from Chinese public universities who had used GenAI for an extended period. The results revealed that GenAI dependency positively predicts false self-efficacy and negatively predicts academic achievement, exhibiting a significant Dunning–Kruger effect. Perceived teacher caring moderates the relationship between GenAI dependency and self-efficacy. High perceived teacher caring mitigates the Dunning–Kruger effect but has a weak moderating effect on academic achievement. These findings enhance the explanatory power of the media system dependency theory in educational contexts and reveal the pathways through which GenAI dependency and teacher caring affect learning processes and outcomes. This study expands the theoretical implications of teacher caring in the digital age and provides empirical evidence to aid higher education administrators in optimising AI governance and teachers in improving instructional interventions. Full article

The rapid advancement of Artificial Intelligence (AI) technologies is driving a profound transformation in higher education, shifting traditional learning toward digital, remote, and AI-mediated environments. This shift—accelerated by the COVID-19 pandemic—has made computer-supported collaborative learning (CSCL) a central pedagogical model for engaging students in virtual, interactive, and peer-based learning. However, while these environments enhance access and flexibility, they also introduce new emotional, social, and intercultural challenges that students must navigate without the benefit of face-to-face interaction. In this evolving context, Social and Emotional Learning (SEL) has become increasingly essential—not only for supporting student well-being but also for fostering the self-efficacy, adaptability, and interpersonal competencies required for success in AI-enhanced academic settings. Despite its importance, the role of SEL in higher education—particularly within CSCL frameworks—remains underexplored. This study investigates how SEL, and specifically cultural empathy, influences students’ learning experiences in multicultural CSCL environments. Grounded in Bandura’s social cognitive theory and Allport’s Contact Theory, this study builds on theoretical insights that position emotional stability, social competence, and cultural empathy as critical SEL dimensions for promoting equity, collaboration, and effective participation in diverse, AI-supported learning settings. A quantitative study was conducted with 258 bachelor’s and master’s students on a multicultural campus. Using the Multicultural Social and Emotional Learning (SEL CASTLE) model, the research examined the relationships among SEL competencies and self-efficacy in CSCL environments. Findings reveal that cultural empathy plays a mediating role between emotional and social competencies and academic self-efficacy, emphasizing its importance in enhancing collaborative learning experiences within AI-driven environments. The results highlight the urgent need to cultivate cultural empathy to support inclusive, effective digital learning across diverse educational settings. This study contributes to the fields of intercultural education and digital pedagogy by presenting the SEL CASTLE model and demonstrating the significance of integrating SEL into AI-supported collaborative learning. Strengthening these competencies is essential for preparing students to thrive in a globally interconnected academic and professional landscape. Full article

The rapid integration of Generative Artificial Intelligence in higher education challenges social work as student adoption outpaces pedagogical guidance. This paper argues that the unguided use of AI fosters cognitive debt: a cumulative deficit in critical thinking, ethical reasoning, and professional judgment that arises from offloading cognitive tasks. To counter this risk, a pedagogical model is proposed, synthesizing experiential learning, andragogy, and critical pedagogies. The framework reframes AI from a passive information tool into an active object of critical inquiry. Through structured assignments across micro, mezzo, and macro practice, the model guides students through cycles of concrete experience with AI, reflective observation of its biases, abstract conceptualization of ethical principles, and active experimentation with responsible professional use. Aligned with professional ethical standards, the model aims to prepare future social workers to scrutinize and shape AI as a tool for social justice. The paper concludes with implications for faculty development, institutional policy, accreditation, and a forward-looking research agenda. Full article

With the widespread integration of Artificial Intelligence-Generated Content (AIGC) into e-commerce platforms, understanding how users perceive, evaluate, and respond to such content has become a critical issue for both academia and industry. This study examines the influence mechanism of AIGC Content Quality (AIGCQ) on users’ Purchase Intention (PI) by constructing a cognitive model centered on Trust (TR) and Perceived Risk (PR). Additionally, it introduces two moderating variables—Ethical Concern (EC) and Perceived Platform Responsibility (PLR)—to explore higher-order psychological influences. The research variables were identified through a systematic literature review and expert interviews, followed by structural equation modeling based on data collected from 507 e-commerce users. The results indicate that AIGCQ significantly reduces users’ PR and enhances TR, while PR negatively and TR positively influence PI, validating the fundamental dual-pathway structure. However, the moderating effects reveal unexpected complexities: PLR simultaneously amplifies both the negative effect of PR and the positive effect of TR on PI, presenting a “dual amplification” pattern; meanwhile, EC weakens the strength of both pathways, manifesting a “dual attenuation” effect. These findings highlight the nonlinear cognitive mechanisms underlying users’ acceptance of AIGC, suggesting that PLR and EC influence decision-making in more intricate ways than previously anticipated. By uncovering the unanticipated patterns in moderation, this study extends the boundary conditions of the trust–risk theoretical framework within AIGC contexts. In practical terms, it reveals that PLR acts as a “double-edged sword,” providing more nuanced guidance for platform governance of AI-generated content, including responsibility frameworks and ethical labeling strategies. Full article

Traumatic brain injury (TBI) remains a major global health problem, contributing significantly to morbidity and mortality worldwide. Despite advances in understanding its complex pathophysiology, current therapeutic strategies are insufficient in addressing the long-term cognitive, emotional, and neurological impairments. While the primary mechanical injury is immediate and unavoidable, the secondary phase involves a cascade of biological processes leading to neuroinflammation, blood–brain barrier (BBB) disruption, and systemic immune activation. The heterogeneity of patient responses underscores the urgent need for reliable biomarkers and targeted interventions. Emerging evidence highlights the gut–brain axis as a critical modulator of the secondary phase, with microbiota-derived extracellular vesicles (MEVs) representing a promising avenue for both diagnosis and therapy. MEVs can cross the intestinal barrier and BBB, carrying biomolecules that influence neuronal survival, synaptic plasticity, and inflammatory signaling. These properties make MEVs promising biomarkers for early detection, severity classification, and prognosis in TBI, while also offering therapeutic potential through modulation of neuroinflammation and promotion of neural repair. MEV-based strategies could enable tailored interventions based on the individual’s microbiome profile, immune status, and injury characteristics. The integration of multi-omics with artificial intelligence is expected to fully unlock the diagnostic and therapeutic potential of MEVs. These approaches can identify molecular subtypes, predict outcomes, and facilitate real-time clinical decision-making. By bridging microbiology, neuroscience, and precision medicine, MEVs hold transformative potential to advance TBI diagnosis, monitoring, and treatment. This review also identifies key research gaps and proposes future directions for MEVs in precision diagnostics and gut microbiota-based therapeutics in neurotrauma care. Full article

Background/Objectives: Surgical ward rounds are central to trainee education but are often associated with stress, cognitive overload, and inconsistent learning. Advances in artificial intelligence (AI), particularly large language models (LLMs), offer new ways to support trainees by simulating ward-round questioning, enhancing preparedness, and reducing anxiety. This study explores the role of generative AI in surgical ward-round education. Methods: Hypothetical plastic and reconstructive surgery ward-round scenarios were developed, including flexor tenosynovitis, DIEP flap monitoring, acute burns, and abscess management. Using de-identified vignettes, AI platforms (ChatGPT-4.5 and Gemini 2.0) generated consultant-level questions and structured responses. Outputs were assessed qualitatively for relevance, educational value, and alignment with surgical competencies. Results: ChatGPT-4.5 showed a strong ability to anticipate consultant-style questions and deliver concise, accurate answers across multiple surgical domains. ChatGPT-4.5 consistently outperformed Gemini 2.0 across all domains, with higher expert Likert ratings for accuracy, clarity, and educational value. It was particularly effective in pre-ward round preparation, enabling simulated questioning that mirrored consultant expectations. AI also aided post-round consolidation by providing tailored summaries and revision materials. Limitations included occasional inaccuracies, risk of over-reliance, and privacy considerations. Conclusions: Generative AI, particularly ChatGPT-4.5, shows promise as a supplementary tool in surgical ward-round education. While both models demonstrated utility, ChatGPT-4.5 was superior in replicating consultant-level questioning and providing structured responses. Pilot programs with ethical oversight are needed to evaluate their impact on trainee confidence, performance, and outcomes. Although plastic surgery cases were used for proof of concept, the findings are relevant to surgical education across subspecialties. Full article

Artificial intelligence (AI) enhances anesthesiology by introducing adaptive systems that improve clinical precision, safety, and responsiveness. This review examines the integration of AI in anesthetic practice, with a focus on closed-loop systems that exemplify autonomous control. These platforms integrate continuous physiologic inputs, such as BIS, EEG, heart rate, and blood pressure, to titrate anesthetic agents in real time, providing more consistent and responsive management than manual methods. Predictive algorithms reduce intraoperative hypotension by up to 40%, and systems such as McSleepy demonstrate greater accuracy in maintaining anesthetic depth and shortening recovery times. In critical care, AI supports sedation management, reduces clinician cognitive load, and standardizes care delivery during high-acuity procedures. The review also addresses the ethical, legal, and logistical challenges to widespread adoption of AI. Key concerns include algorithmic bias, explainability, and accountability for machine-generated decisions and disparities in access due to infrastructure demands. Regulatory frameworks, such as HIPAA and GDPR, are discussed in the context of securing patient data and ensuring its ethical deployment. Additionally, AI may play a transformative role in global health through remote anesthesia delivery and telemonitoring, helping address anesthesiologist shortages in resource-limited settings. Ultimately, AI-guided closed-loop systems do not replace clinicians; instead, they extend their capacity to deliver safe, responsive, and personalized anesthesia. These technologies signal a shift toward robotic anesthesia, where machine autonomy complements human oversight. Continued interdisciplinary development and rigorous clinical validation will determine how AI integrates into both operating rooms and intensive care units. Full article

Artificial Intelligence (AI) is widely recognized as a force that will fundamentally transform traditional chicken farming models. It can reduce labor costs while ensuring welfare and at the same time increase output and quality. However, the breadth of AI’s contribution to chicken farming has not been systematically quantified on a large scale; few people know how far current AI has actually progressed or how it will improve chicken farming to enhance the sector’s sustainability. Therefore, taking “AI + sustainable chicken farming” as the theme, this study retrieved 254 research papers for a comprehensive descriptive analysis from the Web of Science (May 2003 to March 2025) and analyzed AI’s contribution to the sustainable in recent years. Results show that: In the welfare dimension, AI primarily targets disease surveillance, behavior monitoring, stress detection, and health scoring, enabling earlier, less-invasive interventions and more stable, longer productive lifespans. In economic dimension, tools such as automated counting, vision-based weighing, and precision feeding improve labor productivity and feed use while enhancing product quality. In the environmental dimension, AI supports odor prediction, ventilation monitoring, and control strategies that lower emissions and energy use, reducing farms’ environmental footprint. However, large-scale adoption remains constrained by the lack of open and interoperable model and data standards, the compute and reliability burden of continuous multi-sensor monitoring, the gap between AI-based detection and fully automated control, and economic hurdles such as high upfront costs, unclear long-term returns, and limited farmer acceptance, particularly in resource-constrained settings. Environmental applications are also underrepresented because research has been overly vision-centric while audio and IoT sensing receive less attention. Looking ahead, AI development should prioritize solutions that are low cost, robust, animal friendly, and transparent in their benefits so that return on investment is visible in practice, supported by open benchmarks and standards, edge-first deployment, and staged cost–benefit pilots. Technically, integrating video, audio, and environmental sensors into a perception–cognition–action loop and updating policies through online learning can enable full-process adaptive management that improves welfare, enhances resource efficiency, reduces emissions, and increases adoption across diverse production contexts. Full article