Search Results (1,381)

Agentic AI systems are a recently emerged and important approach that goes beyond traditional AI, generative AI, and autonomous systems by focusing on autonomy, adaptability, and goal-driven reasoning. This study provides a clear review of agentic AI systems by bringing together their definitions, frameworks, and architectures, and by comparing them with related areas like generative AI, autonomic computing, and multi-agent systems. To do this, we reviewed 143 primary studies on current LLM-based and non-LLM-driven agentic systems and examined how they support planning, memory, reflection, and goal pursuit. Furthermore, we classified architectural models, input–output mechanisms, and applications based on their task domains where agentic AI is applied, supported using tabular summaries that highlight real-world case studies. Evaluation metrics were classified as qualitative and quantitative measures, along with available testing methods of agentic AI systems to check the system’s performance and reliability. This study also highlights the main challenges and limitations of agentic AI, covering technical, architectural, coordination, ethical, and security issues. We organized the conceptual foundations, available tools, architectures, and evaluation metrics in this research, which defines a structured foundation for understanding and advancing agentic AI. These findings aim to help researchers and developers build better, clearer, and more adaptable systems that support responsible deployment in different domains. Full article

Background: Hypertension remains the leading global risk factor for cardiovascular morbidity and mortality, with suboptimal control rates despite guideline-directed therapies. Digital health and artificial intelligence (AI) technologies offer novel approaches for improving diagnosis, monitoring, and individualized treatment of hypertension. Objectives: To critically review the current landscape of AI-enabled digital tools for hypertension management, including emerging applications, implementation challenges, and future directions. Methods: A narrative review of recent PubMed-indexed studies (2019–2024) was conducted, focusing on clinical applications of AI and digital health technologies in hypertension. Emphasis was placed on real-world deployment, algorithmic explainability, digital biomarkers, and ethical/regulatory frameworks. Priority was given to high-quality randomized trials, systematic reviews, and expert consensus statements. Results: AI-supported platforms—including remote blood pressure monitoring, machine learning titration algorithms, and digital twins—have demonstrated early promise in improving hypertension control. Explainable AI (XAI) is critical for clinician trust and integration into decision-making. Equity-focused design and regulatory oversight are essential to prevent exacerbation of health disparities. Emerging implementation strategies, such as federated learning and co-design frameworks, may enhance scalability and generalizability across diverse care settings. Conclusions: AI-guided titration and digital twin approaches appear most promising for reducing therapeutic inertia, whereas cuffless blood pressure monitoring remains the least mature. Future work should prioritize pragmatic trials with equity and cost-effectiveness endpoints, supported by safeguards against bias, accountability gaps, and privacy risks. Full article

This study presents a novel and interpretable, deployment-ready framework for predicting cybersecurity incidents through item-level behavioral, cognitive, and dispositional indicators. Based on survey data from 453 professionals across countries and sectors, we developed 72 logistic regression models across twelve self-reported incident outcomes—from account lockouts to full device compromise—within six analytically stratified layers (Education, IT, Hungary, UK, USA, and full sample). Drawing on five theoretically grounded domains—cybersecurity behavior, digital literacy, personality traits, risk rationalization, and work–life boundary blurring—our models preserve the full granularity of individual responses rather than relying on aggregated scores, offering rare transparency and interpretability for real-world applications. This approach reveals how stratified models, despite smaller sample sizes, often outperform general ones by capturing behavioral and contextual specificity. Moderately prevalent outcomes (e.g., suspicious logins, multiple mild incidents) yielded the most robust predictions, while rare-event models, though occasionally high in “Area Under the Receiver Operating Characteristic Curve” (AUC), suffered from overfitting under cross-validation. Beyond model construction, we introduce threshold calibration and fairness-aware integration of demographic variables, enabling ethically grounded deployment in diverse organizational contexts. By unifying theoretical depth, item-level precision, multilayer stratification, and operational guidance, this study establishes a scalable blueprint for human-centric cybersecurity. It bridges the gap between behavioral science and risk analytics, offering the tools and insights needed to detect, predict, and mitigate user-level threats in increasingly blurred digital environments. Full article

The convergence of decentralized artificial intelligence (DAI), blockchain technology, and smart contracts is reshaping the design and governance of intelligent systems. As these technologies rapidly evolve, addressing privacy within their architecture, usage models, and associated risks has become increasingly critical. This systematic literature review examines architectural patterns, governance frameworks, real-world applications, and persistent challenges in DAI systems. It identifies prevailing designs such as federated learning integrated with consensus protocols, smart contract-based incentive mechanisms, and decentralized verification methods. Drawing from a diverse body of recent literature, the review highlights implementations across sectors, including healthcare, finance, IoT, autonomous systems, and intelligent infrastructure, each demonstrating significant contributions to privacy, security, and collaborative innovation. Despite these advancements, DAI systems face ongoing obstacles such as scalability limitations, privacy trade-offs, and difficulties with regulatory compliance. The review emphasizes the need for integrative governance approaches that balance transparency, accountability, incentive alignment, and ethical oversight. These elements are proposed as co-evolving pillars essential to establishing trustworthiness in decentralized AI ecosystems. This work offers a comprehensive review for understanding the current landscape and guiding the development of responsible and effective DAI systems in the Web3 era. Full article

This study investigates how the integration of artificial intelligence (AI) transforms job practices within a leading European infrastructure company. Grounded in the Feeling Economy framework, the research explores the shift in task composition following AI implementation, focusing on the emergence of new roles, required competencies, and the ongoing reconfiguration of work. Using a qualitative, single-case study methodology, data were collected through semi-structured interviews with ten employees and company documentation. Thematic analysis revealed five key dimensions: the reconfiguration of job tasks, the improvement of efficiency and quality, psychological and adaptation challenges, the need for AI-related competencies, and concerns about dehumanisation. Findings show that AI systems increasingly assume repetitive and analytical tasks, enabling workers to focus on strategic, empathetic, and creative responsibilities. However, psychological resistance, fears of job displacement, and a perceived erosion of human interaction present implementation barriers. The study provides theoretical contributions by empirically extending the Feeling Economy and task modularisation frameworks. It also offers managerial insights into workforce adaptation, training needs, and the importance of ethical and emotionally intelligent AI integration. Additionally, this study highlights that the Feeling Economy must address AI’s epistemic risks, emphasising fairness, transparency, and participatory governance as essential for trustworthy, emotionally intelligent, and sustainable AI systems. Full article

This article examines how veterinarians in Istanbul experience and navigate the ethical, emotional, and institutional complexities of performing euthanasia on dogs, with particular attention to the differences between companion and street dogs. Drawing on 29 in-depth interviews with private practice veterinarians in Istanbul, this study employs qualitative analysis using the NVivo 12 Plus software and reflexive thematic analysis to identify key patterns in moral reasoning, emotional labor, and clinical decision making. The findings indicate that euthanasia of companion dogs is often framed through shared decision making with guardians, emotional preparation, and post-procedural grief rituals. While still emotionally taxing, these cases are supported by relational presence and mutual acknowledgment. In contrast, euthanasia of street dogs frequently occurs in the absence of legal ownership, institutional accountability, or consistent caregiving, leaving veterinarians to bear the full moral and emotional weight of the decision. Participants described these cases as ethically distinct, marked by relational solitude, clinical ambiguity, and heightened moral distress. Six key themes that reveal how euthanasia becomes a site of both care and conflict when structural support is lacking are identified in this study, including emotional burden, ethical strain, and resistance to routinized killing. By foregrounding the roles of institutional absence and relational asymmetry in shaping end-of-life decisions, this study contributes to empirical veterinary ethics and calls for more contextually attuned ethical frameworks, particularly in urban settings with large populations of street dogs and culturally entrenched practices of collective guardianship and caregiving. Full article

As the fields of plastic surgery and dermatology advance, regenerative medicine is positioned to play a transformative role in both aesthetic and reconstructive procedures. This narrative review examines current and emerging applications of biologic therapies, including exosomes, platelet-rich plasma (PRP), and adipose-derived stem cells (ASCs) with an emphasis on their mechanisms of action, clinical efficacy, and regulatory considerations. We also explore synergistic strategies, such as the combined use of biologics with laser-based technologies, which may enhance therapeutic outcomes. Looking forward, we highlight promising developments in mitochondrial-based therapies, microRNA-based therapies, synthetic exosome mimetics, and AI-assisted biologic design, offering a framework for personalized, precision-driven interventions. By synthesizing existing clinical data alongside scientific and ethical challenges, this narrative review provides a comprehensive perspective on how regenerative therapies are transforming the landscape of aesthetics. Ultimately, successful integration of these innovations will require rigorous validation, ethical responsibility, and a patient-centered approach by plastic surgeons and dermatologists to ensure both safety and accessibility in mainstream practice. Full article

Smoking tobacco is a prominent activity in Cameroon, with each region holding different views on the devices used for smoking. In Bamum, these vessels are called kiis. Many of these pipes, or kiis, have been removed from the kingdom and displayed without proper contextual information in Western institutions since the 1920s. This paper highlights discrepancies in academic pursuits regarding the kii and their decontextualized displays, while also providing ethical guidelines for their future displays. Understanding the intended purpose and cultural significance of a kii allows for the process of restitution in the form of ethical display to take place. Using the Heritage Context Retrieval Analysis (HeCRA) method, the research aim to explore the cultural origins of the kii in the GWU collection, retrieve its cultural context, critique the prevalent cabinet of curiosities display format used in displaying them in museums, and propose ethical frameworks for handling such devices, which are both utilitarian and culturally charged in 21st-century museums. This paper uncovers the true identity of a brass kii and dismantles the cabinet of curiosities and the alignment of African tangible heritage to oddities. The goal is to instigate a new approach to approaching such cultural objects by invoking their original spiritual and cultural symbolism in exhibitions outside of Bamum. Full article

The rapid proliferation of Chat Generative Pre-trained Transformer (ChatGPT) marks a pivotal moment in artificial intelligence, eliciting responses from academic shock to industrial awe. As these technologies advance from passive tools toward proactive, agentic systems, their transformative potential and inherent risks are magnified globally. This paper presents a comprehensive, critical review of ChatGPT’s impact across five key domains: natural language understanding (NLU), content generation, knowledge discovery, education, and engineering. While ChatGPT demonstrates profound capabilities, significant challenges remain in factual accuracy, bias, and the inherent opacity of its reasoning—a core issue termed the “Black Box Conundrum”. To analyze these evolving dynamics and the implications of this shift toward autonomous agency, this review introduces a series of conceptual frameworks, each specifically designed to illuminate the complex interactions and trade-offs within these domains: the “Specialization vs. Generalization” tension in NLU; the “Quality–Scalability–Ethics Trilemma” in content creation; the “Pedagogical Adaptation Imperative” in education; and the emergence of “Human–LLM Cognitive Symbiosis” in engineering. The analysis reveals an urgent need for proactive adaptation across sectors. Educational paradigms must shift to cultivate higher-order cognitive skills, while professional practices (including practices within education sector) must evolve to treat AI as a cognitive partner, leveraging techniques like Retrieval-Augmented Generation (RAG) and sophisticated prompt engineering. Ultimately, this paper argues for an overarching “Ethical–Technical Co-evolution Imperative”, charting a forward-looking research agenda that intertwines technological innovation with vigorous ethical and methodological standards to ensure responsible AI development and integration. Ultimately, the analysis reveals that the challenges of factual accuracy, bias, and opacity are interconnected and acutely magnified by the emergence of agentic systems, demanding a unified, proactive approach to adaptation across all sectors. Full article

Artificial Intelligence (AI) is reshaping industries and workforce demands globally. To ensure that individuals are prepared for an increasingly AI-driven world, it is crucial to develop robust AI literacy and address persistent gender biases in STEM fields. This paper presents a comparative study of AI literacy and gender bias among 192 participants from the United Kingdom and Indonesia. Using a survey-based approach, the study examines participants’ familiarity with AI concepts, confidence in utilizing AI tools, and engagement in ethical discussions related to AI. The findings reveal that while overall AI literacy levels are similar across both countries, UK respondents demonstrate significantly higher familiarity with programming and AI technologies, likely reflecting differences in educational frameworks and digital infrastructure. Moreover, despite widespread use of AI, discussions on its ethical implications remain limited in both countries. The study also highlights persistent gender biases that affect women’s participation and progression in AI and STEM fields; differences in perceptions of gender bias in recruitment, leadership promotion, and support for women suggest that, although progress is being made, significant barriers still exist. The study uncovers nuanced cultural variations in the perception of gender bias: UK participants exhibit greater confidence in gender inclusivity within recruitment and leadership roles, whereas Indonesian respondents report a higher prevalence of targeted initiatives to support women in technology. Overall, this research deepens our understanding of how AI literacy varies across diverse cultural and technological landscapes and offers valuable strategic guidance for tailoring interventions to overcome specific barriers, ultimately supporting innovative developments for women in STEM and women in AI in particular. Full article

This study explores how Instagram fashion influencers build brand loyalty through customer engagement within the framework of sustainable consumption. Grounded in the stimulus–organism–response (SOR) theory, influencer marketing is conceptualized as a stimulus that activates customer engagement (organism), which in turn enhances brand loyalty (response). A cross-sectional survey was conducted with 279 Instagram users in Palestine who actively follow fashion influencers, and the model was tested using partial least squares structural equation modeling (PLS-SEM). The findings confirm that social media influencer marketing (SMIM) significantly improves both engagement and loyalty. Customer engagement was found to be both a partial mediator and a significant moderator, such that highly engaged consumers exhibited stronger loyalty responses—suggesting intensified value alignment and emotional resonance in sustainability contexts. This study extends the prior literature by integrating the creation–consumption–contribution (C–C–C) model into the SOR framework and conceptualizing engagement as both a psychological state and a boundary condition. It contributes to sustainable consumption research by illustrating how participatory digital behaviors can foster ethical brand relationships, particularly in emerging economies. Practically, it offers strategic guidance for fashion brands and influencers to design campaigns that promote co-creation, authenticity, and eco-conscious narratives. It also emphasizes the importance of aligning influencer values with those of sustainability-minded consumers to foster long-term loyalty. By contextualizing the findings within the Palestinian market, the study highlights how cultural factors may shape engagement and sustainability perceptions, paving the way for future cross-cultural investigations. Full article

This study presents preliminary design guidelines to support the evaluation of industrial safety training using immersive technologies, with a focus on high-risk work environments such as working at height. Although virtual reality has been widely adopted for training, few studies have explored its use for behavior-level evaluation, corresponding to Level 3 of the Kirkpatrick Model. Addressing this gap, the study adopts the Design Science Research methodology, combining a systematic literature review with expert focus group analysis to develop a conceptual framework for training evaluation. The results identify key elements necessary for immersive training evaluations, including scenario configuration, ethical procedures, recruitment, equipment selection, experimental design, and implementation strategies. The resulting guidelines are organized into six categories: scenario configuration, ethical procedures, recruitment, equipment selection, experimental design, and implementation strategies. These guidelines represent a DSR-based conceptual artifact to inform future empirical studies and support the structured assessment of immersive safety training interventions. The study also highlights the potential of integrating behavioral and physiological indicators to support immersive evaluations of behavioral change, offering an expert-informed and structured foundation for future empirical studies in high-risk industrial contexts. Full article

The convergence of genomic selection and artificial intelligence (AI) is redefining precision breeding in dairy cattle, enabling earlier, more accurate, and multi-trait selection for health, fertility, climate resilience, and economic efficiency. This review critically examines how advanced genomic tools—such as genome-wide association studies (GWAS), genomic breeding values (GEBVs), machine learning (ML), and deep learning (DL) models to accelerate genetic gain for complex, low heritability traits. Key applications include improved resistance to mastitis and metabolic diseases, enhanced thermotolerance, reduced enteric methane emissions, and increased milk yield. We discuss emerging computational frameworks that combine sensor-derived phenotypes, omics datasets, and environmental data to support data-driven selection decisions. Furthermore, we address implementation challenges related to data integration, model interpretability, ethical considerations, and access in low-resource settings. By synthesizing interdisciplinary advances, this review provides a roadmap for developing AI-augmented genomic selection pipelines that support sustainable, climate-smart, and economically viable dairy systems. Full article

If an arbitrator employs the use of AI to draft an arbitral award, or legal counsel uses AI and the data are leaked within that process, what is the legal consequence, and what will be the ethical concerns and enforceability issues? As artificial intelligence (AI) is used in every field, it has undoubtedly been used within the legal domain. However, its use should be regulated and balanced as there is an adjudication involved between the parties to decide the rights and obligations of the parties. In recent times, AI in arbitration has revolutionized dispute resolution by enhancing efficiency, automating legal research, and expediting case management. However, its application has a different set of challenges attached to it, particularly concerning due process, algorithmic bias, evidentiary integrity, and the enforceability of AI-assisted arbitral awards. This paper critically examines these legal implications, assessing how AI aligns with Indian arbitration laws and international frameworks. It further explores regulatory safeguards, the balanced and ethical use of AI, and the evolving role of arbitrators and counsels in the era of AI. By addressing these concerns, this paper aims to provide a comprehensive analysis of AI’s impact on the legal landscape of arbitration in India. To conclude, this paper proposes an expressed provision within the Arbitration and Conciliation Act, 1996, with respect to disclosure related to the ethical use of AI. Full article

Large language models (LLMs) have the potential to transform healthcare by assisting in documentation, diagnosis, patient communication, and medical education. However, their integration into clinical practice remains a challenge. This perspective explores the barriers to implementation by synthesizing recent evidence across five challenge domains: workflow misalignment and diagnostic safety, bias and equity, regulatory and legal governance, technical vulnerabilities such as hallucinations or data poisoning, and the preservation of patient trust and human connection. While the perspective focuses on barriers, LLM capabilities and mitigation strategies are advancing rapidly, raising the likelihood of near-term clinical impact. Drawing on recent empirical studies, we propose a framework for understanding the key technical, ethical, and practical challenges associated with deploying LLMs in clinical environments and provide directions for future research, governance, and responsible deployment. Full article