Search Results (1,292)

The purpose of this narrative review is to critically appraise recent advances in sports injury rehabilitation—primarily focusing on biopsychosocial (BPS) approaches alongside emerging technological innovations—and identify current gaps and future directions. A literature search was conducted in PubMed, Scopus, and Web of Science for the years 2018–2024. Eligible records were English-language, human studies comprising systematic reviews, clinical trials, and translational investigations on wearable sensors, artificial intelligence (AI), virtual reality (VR), regenerative therapies (platelet-rich plasma [PRP], bone marrow aspirate concentrate [BMAC], stem cells, and prolotherapy), and BPS rehabilitation models; single-patient case reports, editorials, and non-scholarly sources were excluded. The synthesis yielded four themes: (1) BPS implementation remains underutilised owing to a lack of validated tools, variable provider readiness, and system-level barriers; (2) wearables and AI can enhance real-time monitoring and risk stratification but are limited by data heterogeneity, non-standardised pipelines, and sparse external validation; (3) VR/gamification improves engagement and task-specific practice, but evidence is dominated by pilot or laboratory studies with scarce longitudinal follow-up data; and (4) regenerative interventions show mechanistic promise, but conclusions are constrained by methodological variability and regulatory hurdles. Conclusions: BPS perspectives and emerging technologies have genuine potential to improve outcomes, but translation to practice hinges on (1) pragmatic or hybrid effectiveness–implementation trials, (2) standardisation of data and intervention protocols (including core outcome sets and effect-size reporting), and (3) integration of psychological and social assessment into routine pathways supported by provider training and interoperable digital capture. Full article

Achieving consensus in group decision-making often involves overcoming significant challenges, particularly reconciling diverse perspectives and mitigating biases hindering agreement. Traditional methods relying on human facilitators are usually constrained by scalability and efficiency, especially in large-scale, fast-paced discussions. To address these challenges, this study proposes a novel real-time facilitation framework, employing large language models (LLMs) as automated facilitators within a custom-built multi-user chat system. This framework is distinguished by its real-time adaptive system architecture, which enables dynamic adjustments to facilitation strategies based on ongoing discussion dynamics. Leveraging cosine similarity as a core metric, this approach evaluates the ability of three state-of-the-art LLMs—ChatGPT 4.0, Mistral Large 2, and AI21 Jamba-Instruct—to synthesize consensus proposals that align with participants’ viewpoints. Unlike conventional techniques, the system integrates adaptive facilitation strategies, including clarifying misunderstandings, summarizing discussions, and proposing compromises, enabling the LLMs to refine consensus proposals based on user feedback iteratively. Experimental results indicate that ChatGPT 4.0 achieved the highest alignment with participant opinions and required fewer iterations to reach consensus. A one-way ANOVA confirmed that differences in performance between models were statistically significant. Moreover, descriptive analyses revealed nuanced differences in model behavior across various sustainability-focused discussion topics, including climate action, quality education, good health and well-being, and access to clean water and sanitation. These findings highlight the promise of LLM-driven facilitation for improving collective decision-making processes and underscore the need for further research into robust evaluation metrics, ethical considerations, and cross-cultural adaptability. Full article

Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collaboration (HMC). Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments, where adaptive autonomy and intuitive interaction are essential. Traditional UAV autonomy has relied mainly on visual perception or preprogrammed planning, offering limited adaptability and explainability. This study introduces a novel reference architecture, the multimodal AI–HMC system, based on which a dedicated UAV use case architecture was instantiated and experimentally validated in a controlled laboratory environment. The architecture integrates VLM-powered reasoning, real-time depth estimation, and natural-language interfaces, enabling UAVs to perform context-aware actions while providing transparent explanations. Unlike prior approaches, the system generates navigation commands while also communicating the underlying rationale and associated confidence levels, thereby enhancing situational awareness and fostering user trust. The architecture was implemented in a real-time UAV navigation platform and evaluated through laboratory trials. Quantitative results showed a 70% task success rate in single-obstacle navigation and 50% in a cluttered scenario, with safe obstacle avoidance at flight speeds of up to 0.6 m/s. Users approved 90% of the generated instructions and rated explanations as significantly clearer and more informative when confidence visualization was included. These findings demonstrate the novelty and feasibility of embedding VLMs into UAV systems, advancing explainable, human-centric autonomy and establishing a foundation for future multimodal AI applications in HMC, including robotics. Full article

As AI recommendation systems become increasingly important in consumer decision-making, leveraging sound cues to optimize user interaction experience has become a key research topic. Grounded in the theory of perceptual contagion, this study centers on sound cues in AI recommendation scenarios, systematically examining their impact on consumer choice and choice satisfaction, as well as the underlying psychological mechanisms. Study 1 (hotel recommendation, N = 155) demonstrated that embedding sound cues into recommendation interfaces significantly increased consumer choice and choice satisfaction. Study 2 (laptop recommendation, N = 155) further revealed that this effect was mediated by preference fluency. Contrary to expectations, AI literacy did not moderate these effects, suggesting that sound cues exert influence across different user groups regardless of technological expertise. Theoretically, this study (1) introduces the theory of perceptual contagion into AI-human interaction research; (2) identifies preference fluency as the core mediating mechanism; and (3) challenges the traditional assumptions about the role of AI literacy. Practically, this study proposes a low-cost and highly adaptable design strategy, providing a new direction for recommendation systems to shift from content-driven to experience-driven. These findings enrich the understanding of sensory influences in digital contexts and offer practical insights for optimizing the design of AI platforms. Full article

Deep learning (DL) has revolutionized medical image analysis (MIA), enabling early anomaly detection, precise lesion segmentation, and automated disease classification. However, its clinical integration faces two major challenges: reliance on limited, narrowly annotated datasets that inadequately capture real-world patient diversity, and the inherent “black-box” nature of DL decision-making, which complicates physician scrutiny and accountability. Eye tracking (ET) technology offers a transformative solution by capturing radiologists’ gaze patterns to generate supervisory signals. These signals enhance DL models through two key mechanisms: providing weak supervision to improve feature recognition and diagnostic accuracy, particularly when labeled data are scarce, and enabling direct comparison between machine and human attention to bridge interpretability gaps and build clinician trust. This approach also extends effectively to multimodal learning models (MLMs) and vision–language models (VLMs), supporting the alignment of machine reasoning with clinical expertise by grounding visual observations in diagnostic context, refining attention mechanisms, and validating complex decision pathways. Conducted in accordance with the PRISMA statement and registered in PROSPERO (ID: CRD42024569630), this review synthesizes state-of-the-art strategies for ET-DL integration. We further propose a unified framework in which ET innovatively serves as a data efficiency optimizer, a model interpretability validator, and a multimodal alignment supervisor. This framework paves the way for clinician-centered AI systems that prioritize verifiable reasoning, seamless workflow integration, and intelligible performance, thereby addressing key implementation barriers and outlining a path for future clinical deployment. Full article

Antimicrobial resistance (AMR) in livestock production systems has emerged as a major global health concern, threatening not only animal welfare and agricultural productivity but also food safety and public health. The widespread, and often poorly regulated, use of antimicrobials for growth promotion, prophylaxis, and metaphylaxis has accelerated the emergence and dissemination of resistant bacteria and resistance genes. These elements circulate across interconnected animal, environmental, and human ecosystems, driven by mobile genetic elements and amplified through the food production chain. It is estimated that more than two-thirds of medically important antimicrobials are used in animals, and AMR could cause millions of human deaths annually by mid-century if unchecked. In some livestock systems, multidrug-resistant E. coli prevalence already exceeds half of isolates, particularly in poultry and swine in low- and middle-income countries (LMICs). This narrative review provides a comprehensive overview of the molecular epidemiology, ecological drivers, and One Health implications of AMR in food-producing animals. We highlight key zoonotic and foodborne bacterial pathogens—including Escherichia coli, Salmonella enterica, and Staphylococcus aureus—as well as underappreciated reservoirs in commensal microbiota and livestock environments. Diagnostic platforms spanning phenotypic assays, PCR, MALDI-TOF MS, whole-genome sequencing, and CRISPR-based tools are examined for their roles in AMR detection, surveillance, and resistance gene characterization. We also evaluate current antimicrobial stewardship practices, global and regional surveillance initiatives, and policy frameworks, identifying critical implementation gaps, especially in low- and middle-income countries. Emerging sectors such as aquaculture and insect farming are considered for their potential role as future AMR hotspots. Finally, we outline future directions including real-time genomic surveillance, AI-assisted resistance prediction, and integrated One Health data platforms as essential innovations to combat AMR. Mitigating the threat of AMR in animal agriculture will require coordinated scientific, regulatory, and cross-sectoral responses to ensure the long-term efficacy of antimicrobial agents for both human and veterinary medicine. Full article

The aviation industry prioritizes safety and has embraced innovative approaches for both reactive and proactive safety measures. Machine learning (ML) has emerged as a useful tool for aviation safety. This systematic literature review explores ML applications for safety within the aviation industry over the past 25 years. Through a comprehensive search on Scopus and backward reference searches via Google Scholar, 87 of the most relevant papers were identified. The investigation focused on the application context, ML techniques employed, data sources, and the implications of contextual nuances for safety analysis outcomes. ML techniques have been effective for post-accident analysis, predictive, and real-time incident detection across diverse aviation scenarios. Supervised, unsupervised, and semi-supervised learning methods, including neural networks, decision trees, support vector machines, and deep learning models, have all been applied for analyzing accidents, identifying patterns, and forecasting potential incidents. Notably, data sources such as the Aviation Safety Reporting System (ASRS) and the National Transportation Safety Board (NTSB) datasets were the most used. Transparency, fairness, and bias mitigation emerge as critical factors that shape the credibility and acceptance of ML-based safety research in aviation. The review revealed seven recommended future research directions: (1) interpretable AI; (2) real-time prediction; (3) hybrid models; (4) handling of unbalanced datasets; (5) privacy and data security; (6) human–machine interface for safety professionals; (7) regulatory implications. These directions provide a blueprint for further ML-based aviation safety research. This review underscores the role of ML applications in shaping aviation safety practices, thereby enhancing safety for all stakeholders. It serves as a constructive and cautionary guide for researchers, practitioners, and decision-makers, emphasizing the value of ML when used appropriately to transform aviation safety to be more data-driven and proactive. Full article

This systematic review examines the role of artificial intelligence (AI) in the development of sustainable mental health interventions through a comprehensive analysis of literature published between 2020 and 2025. In accordance with the PRISMA guidelines, 62 studies were selected from 1652 initially identified records across four major databases. The results revealed four dimensions critical for sustainability: ethical considerations (privacy, informed consent, bias, and human oversight), personalization approaches (federated learning and AI-enhanced therapeutic interventions), risk mitigation strategies (data security, algorithmic bias, and clinical efficacy), and implementation challenges (technical infrastructure, cultural adaptation, and resource allocation). The findings demonstrate that long-term sustainability depends on ethics-driven approaches, resource-efficient techniques such as federated learning, culturally adaptive systems, and appropriate human-AI integration. The study concludes that sustainable mental health AI requires addressing both technical efficacy and ethical integrity while ensuring equitable access across diverse contexts. Future research should focus on longitudinal studies examining the long-term effectiveness and cultural adaptability of AI interventions in resource-limited settings. Full article

In this systematic literature review, we examined the integration of emotional intelligence into artificial intelligence (AI) systems, focusing on advancements, challenges, and opportunities in emotion classification technologies. Accurate emotion recognition in AI holds immense potential in healthcare, the IoT, and education. However, challenges such as computational demands, limited dataset diversity, and real-time deployment complexity remain significant. In this review, we included research on emerging solutions like multimodal data processing, attention mechanisms, and real-time emotion tracking to address these issues. By overcoming these issues, AI systems enhance human–AI interactions and expand real-world applications. Recommendations for improving accuracy and scalability in emotion-aware AI are provided based on the review results. Full article

Neuromuscular robotic prostheses have emerged as a critical convergence point between biomedical engineering, machine learning, and human–machine interfaces. This work provides a narrative state-of-the-art review regarding recent developments in robotic prosthetic technology, emphasizing sensor integration, actuator architectures, signal acquisition, and algorithmic strategies for intent decoding. Special focus is given to non-invasive biosignal modalities, particularly surface electromyography (sEMG), as well as invasive approaches involving direct neural interfacing. Recent developments in AI-driven signal processing, including deep learning and hybrid models for robust classification and regression of user intent, are also examined. Furthermore, the integration of real-time adaptive control systems with surgical techniques like Targeted Muscle Reinnervation (TMR) is evaluated for its role in enhancing proprioception and functional embodiment. Finally, this review highlights the growing importance of modular, open-source frameworks and additive manufacturing in accelerating prototyping and customization. Progress in this domain will depend on continued interdisciplinary research bridging artificial intelligence, neurophysiology, materials science, and real-time embedded systems to enable the next generation of intelligent prosthetic devices. Full article

The widespread adoption of Artificial Intelligence (AI) in critical domains, such as healthcare, finance, law, and autonomous systems, has brought unprecedented societal benefits. Its black-box (sub-symbolic) nature allows AI to compute prediction without explaining the rationale to the end user, resulting in lack of transparency between human and machine. Concerns are growing over the opacity of such complex AI models, particularly deep learning architectures. To address this concern, explainability is of paramount importance, which has triggered the emergence of Explainable Artificial Intelligence (XAI) as a vital research area. XAI is aimed at enhancing transparency, trust, and accountability of AI models. This survey presents a comprehensive overview of XAI from the dual perspectives of challenges and opportunities. We analyze the foundational concepts, definitions, terminologies, and taxonomy of XAI methods. We then review several application domains of XAI. Special attention is given to various challenges of XAI, such as no universal definition, trade-off between accuracy and interpretability, and lack of standardized evaluation metrics. We conclude by outlining the future research directions of human-centric design, interactive explanation, and standardized evaluation frameworks. This survey serves as a resource for researchers, practitioners, and policymakers to navigate the evolving landscape of interpretable and responsible AI. 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 study presents a human-centric, data-driven modeling framework for the intelligent evaluation and classification of vibration-reducing (VR) gloves used in hand-transmitted vibration environments. Recognizing the trade-offs between protection and functionality, the integrated performance assessment incorporates three critical and often conflicting metrics: manual dexterity, grip strength, and distributed vibration transmissibility at the palm and fingers. Three independent experiments involving fifteen participants were conducted to evaluate the individual performance of ten commercially available VR gloves fabricated from air bladders, polymers, and viscoelastic gels. The effects of VR gloves on manual dexterity, grip strength, and distributed vibration transmission were investigated. The resulting experimental data were used to train and tune seven different machine learning models. The results suggested that the AdaBoost model demonstrated superior predictive performance, achieving 92% accuracy in efficiently evaluating the integrated performance of VR gloves. It is further shown that the proposed data-driven model could be effectively applied to classify the performances of VR gloves in three workplace conditions based on the dominant vibration frequencies (low-, medium-, and high-frequency). The proposed framework demonstrates the potential of AI-enhanced intelligent actuation systems to support personalized selection of wearable protective equipment, thereby enhancing occupational safety, usability, and task efficiency in vibration-intensive environments. Full article

This paper contributes conceptually and empirically to a more rigorous understanding of the role of intellectual capital in the sustainable development of the BRICS+ bloc. We investigate the growing command of technical competencies over social competencies across the entire knowledge process. A range of factors, including the ever-increasing tension between AI and humans, the multidimensional nature of intellectual capital, and a focus on competency-based approaches, shape the theory of a knowledge economy. This study presents a spatial modeling approach to analyze the sustainable development of economic systems, reevaluates the importance of intellectual capital in the era of Industry 4.0, introduces the concept of scientific management of intellectual capital by categorizing it into the AI, individual, and collective human mind, and enhances the methodology of managing the knowledge economy to foster intellectual capital development. The primary finding of the research is that the advancement of the knowledge economy is driving digital communication and network-based collaboration on a larger scale within the BRICS+ bloc. Policy implications are intricately linked to the necessity for the holistic development of intellectual capital, encompassing both human and artificial intelligence. This development requires enhancements in quality of life and living standards, advancements in education and healthcare, optimization of the labor market, and reinforcing its connection with the educational sector. Concurrently, it is vital to stimulate research and development (R&D), support the commercialization of high-tech innovations, and accelerate the process of robotization. These combined efforts are essential to fostering economic growth effectively. 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