Search Results (3,353)

Empathic behavior is increasingly incorporated into socially assistive robots, yet little is known about how older adults’ personality-based self-regulatory processes shape responses to such designs. The present study examined a recognition-based “mirror effect” framework of narcissistic self-regulation, referring to the ways individuals maintain a valued self-image through social feedback and acknowledgment. We focused on two core dimensions: narcissistic admiration, characterized by self-promotion and the pursuit of affirmation, and narcissistic rivalry, characterized by defensiveness, antagonism, and sensitivity to threat. Community-dwelling older adults (N = 527; M_age = 72.73) were randomly assigned to view a video of a socially assistive robot interacting in either an empathic or a cold manner. Participants reported their perceived recognition by the robot, defined as the subjective experience of feeling seen, acknowledged, and valued, as well as multiple robot evaluations (anthropomorphism, likability, perceived intelligence, safety, and intention to use). At the mean level, empathic robot behavior increased perceived recognition, anthropomorphism, and likability but did not improve perceived intelligence, safety, or intention to use. Conditional process analyses revealed that narcissistic admiration was positively associated with perceived recognition, which in turn predicted more favorable robot evaluations, regardless of robot behavior. In contrast, narcissistic rivalry showed a behavior-dependent pattern: rivalry was associated with reduced perceived recognition and less favorable evaluations primarily in the empathic condition, whereas this association reversed in the cold condition. Importantly, once perceived recognition and narcissistic traits were accounted for, the cold robot was evaluated as more intelligent, safer, and more desirable to use than the empathic robot. Studying these processes in older adults is theoretically and practically significant, as later life is marked by shifts in social roles, autonomy concerns, and sensitivity to interpersonal evaluation, which may alter how empathic technologies are experienced. Together, the findings identify perceived recognition as a central psychological mechanism linking personality and robot design and suggest that greater robotic empathy is not universally beneficial, particularly for users high in rivalry-related threat sensitivity. Full article

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is one of the most common extra-articular manifestations of rheumatoid arthritis (RA) and a leading cause of mortality in RA patients. The diverse and nonspecific clinical presentations of RA-ILD make early diagnosis particularly challenging. In recent years, with a deeper understanding of the pathogenesis of RA-ILD and rapid advancements in medical imaging, artificial intelligence (AI) technologies, and biomarker research, notable progress has been achieved in the diagnostic approaches for RA-ILD. This review summarizes the latest research developments in the diagnosis of RA-ILD, with a focus on the clinical practice guidelines released in 2025. It discusses the application of high-resolution computed tomography (HRCT), the potential of AI in assisting HRCT-based diagnosis, and the discovery and validation of biomarkers. Furthermore, the review addresses current diagnostic challenges and explores future directions, providing clinicians and researchers with a cutting-edge perspective on RA-ILD diagnosis. Full article

This study aims to address the safety and mobility challenges faced by visually impaired individuals. To this end, a lightweight, high-precision semantic segmentation network is proposed for scenes containing tactile paving and zebra crossings. The network is successfully deployed on an intelligent guide robot equipped with a high-definition camera and a Huawei Atlas 310 embedded computing platform. To enhance both real-time performance and segmentation accuracy on resource-constrained devices, an improved G-GhostNet backbone is designed for feature extraction. Specifically, it is combined with a depthwise separable convolution-based Coordinate Attention module and a redesigned Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contextual features. A dedicated decoder efficiently fuses multi-level features to refine segmentation of tactile paving and zebra crossings. Experimental results demonstrate that the proposed model achieves mPA of 97% and 93%, mIoU of 94% and 86% for tactile paving and zebra crossing segmentation, respectively, with an inference speed of 59.2 fps. These results significantly outperform several mainstream semantic segmentation networks, validating the effectiveness and practical value of the proposed method in embedded systems for visually impaired travel assistance. Full article

Generative artificial intelligence (GenAI), particularly large language models (LLMs) such as ChatGPT and DeepSeek, is transforming healthcare by enhancing clinical decision-making, education, and patient interaction. This exploratory study compares the responses of ChatGPT (GPT-4.1) and DeepSeek-V2 against 90 final-year physiotherapy students in Greece on the quality of the responses to 60 clinical questions across four rehabilitation domains: low back pain, multiple sclerosis, frozen shoulder, and knee osteoarthritis (15 questions per domain). The questions spanned basic knowledge, diagnosis, alternative treatments, and rehabilitation practices. The responses were evaluated for their relevance, accuracy, clarity, completeness, and consistency with clinical practice guidelines (CPGs), emphasizing conceptual understanding. This study provides novel contributions by (i) benchmarking LLMs in physiotherapy-specific domains (low back pain, multiple sclerosis, frozen shoulder, and knee osteoarthritis) underrepresented in prior AI-health evaluations; (ii) directly comparing the LLM written response quality to student performance under exam constraints; and (iii) highlighting the improvement potential for education, complementing ChatGPT’s established role in physician decision support. The results indicate that the LLMs produced higher-quality written responses than students in most domains, particularly in the global response quality and the conceptual depth of written responses, highlighting their potential as educational aids for knowledge-based tasks, although not equivalent to clinical expertise. This suggests AI’s role in physiotherapy as a supportive tool rather than a replacement for hands-on clinical skills and asks whether GenAI could transform physiotherapy practice by augmenting, rather than threatening, human-centered care, for its potential as a knowledge support tool in education, pending validation in clinical contexts. This study explores these findings, compares them with the related work, and discusses whether GenAI will transform or threaten physiotherapy practice. Ethical considerations, limitations, and future directions, including AI voice assistants and AI characters, are addressed. Full article

Background: Artificial intelligence (AI) is transforming medical practice, enhancing diagnostic accuracy, personalisation, and clinical efficiency. However, this transition raises complex ethical challenges related to transparency, accountability, fairness, and human oversight. This study examines how the literature conceptualises and distributes ethical responsibility in AI-assisted healthcare. Methods: This semi-systematic, theory-informed thematic review was conducted in accordance with the PRISMA 2020 guidelines. Publications from 2020 to 2025 were retrieved from PubMed, ScienceDirect, IEEE Xplore databases, and MDPI journals. A semi-quantitative keyword-based scoring model was applied to titles and abstracts to determine their relevance. High-relevance studies (n = 187) were analysed using an eight-category ethical framework: transparency and explainability, regulatory challenges, accountability, justice and equity, patient autonomy, beneficence–non-maleficence, data privacy, and the impact on the medical profession. Results: The analysis revealed a fragmented ethical landscape in which technological innovation frequently outperforms regulatory harmonisation and shared accountability structures. Transparency and explainability were the dominant concerns (34.8%). Significant gaps in organisational responsibility, equitable data practices, patient autonomy, and professional redefinition were reported. A multilevel ethical responsibility model was developed, integrating micro (clinical), meso (institutional), and macro (regulatory) dimensions, articulated through both ex ante and ex post perspectives. Conclusions: AI requires governance frameworks that integrate ethical principles, regulatory alignment, and epistemic justice in medicine. This review proposes a multidimensional model that bridges normative ethics and operational governance. Future research should explore empirical, longitudinal, and interdisciplinary approaches to assess the real impact of AI on clinical practice, equity, and trust. Full article

Background: Developmental coordination Disorder (DCD) is a prevalent and persistent neurodevelopmental condition characterized by motor learning difficulties that significantly affect daily functioning and participation. Despite growing interest in artificial intelligence (AI) applications within healthcare, the extent and nature of AI use in the evaluation and intervention of DCD remain unclear. Objective: This scoping review aimed to systematically map the existing literature on the use of AI and AI-assisted approaches in the evaluation, screening, monitoring, and intervention of DCD, and to identify current trends, methodological characteristics, and gaps in the evidence base. Methods: A scoping review was conducted in accordance with the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines and was registered on the Open Science Framework. Systematic searches were performed in Scopus, PubMed, Web of Science, and IEEE Xplore, supplemented by snowballing. Peer-reviewed studies applying AI methods to DCD-relevant populations were included. Data was extracted and charted to summarize study designs, populations, AI methods, data modalities, clinical purposes, outcomes, and reported limitations. Results: Seven studies published between 2021 and 2025 met the inclusion criteria following a literature search covering the period from January 2010 to 2025. One study listed as 2026 was included based on its early access online publication in 2025. Most studies focused on AI applications for assessment, screening, and classification, using supervised machine learning or deep learning models applied to movement-based data, wearable sensors, video recordings, neurophysiological signals, or electronic health records. Only one randomized controlled trial evaluated an AI-assisted intervention. The evidence base was dominated by early-phase development and validation studies, with limited external validation, heterogeneous diagnostic definitions, and scarce intervention-focused research. Conclusions: Current AI research in DCD is primarily centered on evaluation and early identification, with comparatively limited evidence supporting AI-assisted intervention or rehabilitation. While existing findings suggest that AI has the potential to enhance objectivity and sensitivity in DCD assessment, significant gaps remain in clinical translation, intervention development, and implementation. Future research should prioritize theory-informed, clinician-centered AI applications, including adaptive intervention systems and decision-support tools, to better support occupational therapy and physiotherapy practice in DCD care. Full article

Engineering is suffering a significant change driven by the integration of artificial intelligence (AI) into engineering optimization in design, analysis, and operational efficiency across numerous disciplines. This review synthesizes the current landscape of AI-driven optimization methodologies and their impacts on engineering applications. In the literature, several frameworks for AI-based engineering optimization have been identified: (1) machine learning models are trained as objective and constraint functions for optimization problems; (2) machine learning techniques are used to improve the efficiency of optimization algorithms; (3) neural networks approximate complex simulation models such as finite element analysis (FEA) and computational fluid dynamics (CFD) and this makes it possible to optimize complex engineering systems; and (4) machine learning predicts design parameters/initial solutions that are subsequently optimized. Fundamental AI technologies, such as artificial neural networks and deep learning, are examined in this paper, along with commonly used AI-assisted optimization strategies. Representative applications of AI-driven engineering optimization have been surveyed in this paper across multiple fields, including mechanical and aerospace engineering, civil engineering, electrical and computer engineering, chemical and materials engineering, energy and management. These studies demonstrate how AI enables significant improvements in computational modelling, predictive analytics, and generative design while effectively handling complex multi-objective constraints. Despite these advancements, challenges remain in areas such as data quality, model interpretability, and computational cost, particularly in real-time environments. Through a systematic analysis of recent case studies and emerging trends, this paper provides a critical assessment of the state of the art and identifies promising research directions, including physics-informed neural networks, digital twins, and human–AI collaborative optimization frameworks. The findings highlight AI’s potential to redefine engineering optimization paradigms, while emphasizing the need for robust, scalable, and ethically aligned implementations. Full article

The increasing urgency to mitigate plastic pollution has accelerated the shift from linear manufacturing toward circular systems. This review synthesizes current advances in mechanical, chemical, biological, and upcycling pathways, emphasizing how artificial intelligence (AI) is reshaping decision-making, performance prediction, and system-level optimization. Intelligent sensing technologies—such as FTIR, Raman spectroscopy, hyperspectral imaging, and LIBS—combined with Machine Learning (ML) classifiers have improved material identification, reduced reject rates, and enhanced sorting precision. AI-assisted kinetic modeling, catalyst performance prediction, and enzyme design tools have improved process intensification for pyrolysis, solvolysis, depolymerization, and biocatalysis. Life Cycle Assessment (LCA)-integrated datasets reveal that environmental benefits depend strongly on functional-unit selection, energy decarbonization, and substitution factors rather than mass-based comparisons alone. Case studies across Europe, Latin America, and Asia show that digital traceability, Extended Producer Responsibility (EPR), and full-system costing are pivotal to robust circular outcomes. Upcycling strategies increasingly generate high-value materials and composites, supported by digital twins and surrogate models. Collectively, evidence indicates that AI moves from supportive instrumentation to a structural enabler of transparency, performance assurance, and predictive environmental planning. The convergence of AI-based design, standardized LCA frameworks, and inclusive governance emerges as a necessary foundation for scaling circular plastic systems sustainably. Full article

The application of Generative Artificial Intelligence (GenAI)—defined as a class of AI systems capable of autonomously generating new content such as images, texts, and design solutions based on learned data patterns—has become increasingly widespread in creative design. By supporting ideation, rapid trial-and-error, and data-driven decision-making, GenAI enables designers to explore design alternatives more efficiently and enhances human–computer interaction experiences. In design practice, GenAI functions not only as a productivity-enhancing tool but also as a collaborative partner that assists users in visual exploration, concept refinement, and iterative development. However, users still face a certain learning curve before effectively adopting these technologies. Within the framework of human-centered artificial intelligence, contemporary design practices place greater emphasis on inclusivity across diverse user groups and on enabling intuitive “what-you-think-is-what-you-get” interaction experiences. From a sustainable design perspective, GenAI’s capabilities in digital simulation, rapid iteration, and automated feedback contribute to more efficient design workflows, reduced collaboration costs, and broader access to creative participation for users with varying levels of expertise. These characteristics play a crucial role in enhancing the accessibility of design resources and supporting the long-term sustainability of creative processes. Focusing on the context of China’s digital design industry, this study investigates the application of GenAI in design workflows through an empirical case study of Zhitu AI, a generative design tool developed by Beijing Didi Infinity Technology Development Co., Ltd. The study conducts a literature review to outline the role of GenAI in visual design processes and employs observation-based experiments and semi-structured interviews with users of varying levels of design expertise. The findings reveal key pain points across stages such as prompt formulation, secondary editing, and asset generation. Drawing on the Kano model, the study further identifies potential design opportunities and discusses their value in improving efficiency, supporting non-expert users, and promoting more sustainable and inclusive design practices. Full article

Artificial intelligence (AI) has revolutionized medical image analysis. Several neural network (NN) architectures were developed and applied across the last decade, becoming essential for automated diagnosis and clinical applications. AI based on NNs has become increasingly integrated into gastroenterology, offering new opportunities for automated lesion detection and workflow optimization. Small-bowel capsule endoscopy (SBCE) has benefited substantially from these advances, addressing long-standing challenges such as time-consuming video review and variability among readers. This systematic review and meta-analysis evaluated neural network-based models for lesion detection in SBCE, assessing pooled diagnostic accuracy and the impact of AI on reading time. A total of 44 primary studies were included: 36 validation studies for accuracy and 9 clinical studies for reading time. All NN architectures demonstrated high diagnostic performance, with a pooled accuracy of 95.3% (95% CI: 94.1–96.5%). More recent architectures, including transformer-based and capsule networks, outperformed classical convolutional neural networks (CNNs). AI assistance significantly reduced SBCE reading time, with a pooled mean reduction of 84% compared to standard review. These findings highlight the strong potential of AI to enhance SBCE efficiency and diagnostic reliability. Full article

This study analyzes the observed patterns of Generative Artificial Intelligence (Generative AI) use by students in higher education through the lens of the sociotechnical systems (STS) theory, focusing on the academic subsystem. The empirical basis is a survey of 2083 students (3686 responses) from seven countries in Central and Eastern Europe, Central Asia, and Central Africa. Based on these data, two proxy indicators are proposed: A₁, reflecting the overall academic use of Generative AI and A₂, characterizing the use of Generative AI in a research context. The results show that Generative AI is widely incorporated into students’ academic activities (A₁ = 79.06%), while research-oriented use remains less common (A₂ = 46.66%) and varies significantly across subsamples. A joint analysis of A₁ and A₂, visualized as a zoned space A₁–A₂, reveals different configurations of academic practices: from a predominance of routine educational use to a more pronounced focus on research tasks. Cross-country comparisons show that in certain contexts (e.g., Kazakhstan and one of the Ukrainian subsamples), Generative AI is more often used in a research context, while in other cases, its use remains predominantly educational and routine. In this sense, the results indicate that Generative AI is beginning to fulfill the role of an emerging student research assistant in students’ academic life: technology has already become a familiar tool for completing educational tasks, but its use in supporting research activities remains fragmented. The proposed model and proxy indicators allow us to describe and compare current configurations of Generative AI use in the academic subsystem. The obtained results provide a basis for further research aimed at a deeper understanding of the factors determining the inclusion of Generative AI in student research practice, as well as for the development of management approaches regarding its use in higher education. Full article

Manual alignment between the trocar, surgical instrument, and robot during minimally invasive surgery (MIS) can be time-consuming and error-prone, and many existing systems do not provide autonomous localization and pose estimation. This paper presents an artificial intelligence (AI)-assisted, vision-guided framework for automated localization and positioning of the ATHENA parallel surgical robot. The proposed approach combines an Intel RealSense RGB–depth (RGB-D) camera with a You Only Look Once version 11 (YOLO11) object detection model to estimate the 3D spatial coordinates of key surgical components in real time. The estimated coordinates are streamed over Transmission Control Protocol/Internet Protocol (TCP/IP) to a programmable logic controller (PLC) using Modbus/TCP, enabling closed-loop robot positioning for automated docking. Experimental validation in a controlled setup designed to replicate key intraoperative constraints demonstrated submillimeter positioning accuracy (≤0.8 mm), an average end-to-end latency of 67 ms, and a 42% reduction in setup time compared with manual alignment, while remaining robust under variable lighting. These results indicate that the proposed perception-to-control pipeline is a practical step toward reliable autonomous robotic docking in MIS workflows. Full article

With the sustained expansion of global meat consumption, advanced freezing and thawing technologies have become essential to preserve quality and extend shelf life within the food supply chain. This review systematically consolidates recent progress by examining fundamental principles, conventional techniques, emerging multi-physics methods (e.g., high-pressure-, ultrasound-, and electric field-assisted processing), and the integration of artificial intelligence (AI). It details the mechanism of ice-crystal formation and its impact on meat quality attributes. While conventional methods remain prevalent, their limitations in controlling ice crystallization are evident. Emerging technologies demonstrate superior capability in regulating ice morphology, thereby mitigating cellular damage. AI applications, including numerical simulation, quality monitoring via machine learning, and predictive modeling of thawing parameters, show considerable potential to enhance processing efficiency—though challenges in data scarcity and model generalizability remain. Collectively, these advancements form an integrated “theory–technology–intelligence” framework, supporting the development of more sustainable, efficient, and quality-focused meat processing systems. Full article

The growth of generative artificial intelligence (GAI), remarkably, Large Language Models (LLMs) such as ChatGPT, converts the educational environment by empowering intelligent, data-driven education and curriculum design innovation. This study aimed to assess the integration of LLMs into higher education to foster curriculum design, learning outcomes, and innovative work behaviour (IWB). Specifically, this study investigated how LLMs’ perceived usefulness (PU) and perceived ease of use (PEOU) can support educators to be engaged in IWB—idea generation (IG), idea promotion (IP), opportunity exploration (OE), and reflection (Relf)—employing a web-based survey and targeting faculty members. A total of 493 replies were obtained and found to be valid to be analysed with partial least squares structural equation modelling (PLS-SEM). The results indicated that PU and PEOU have a significant positive impact on the four dimensions of IWB in the context of LLMs for curriculum development. The evaluated model can assist in bridging the gap between AI technology acceptance and educational strategy by offering some practical evidence and implications for university leaders and policymakers. Additionally, this study offered a data-driven pathway to advance higher education IWB through the adoption of LLMs. Full article

Background: Assistive technologies (ATs) for individuals with tetraplegia have evolved from mechanical aids to complex neurotechnological, digital, and psychosocial systems. However, the evidence base remains fragmented, with heterogeneous methodologies and limited integration across domains. This review synthesizes recent review-level evidence to clarify current trends, gaps, and directions in ATs for tetraplegia. Methods: A narrative review of reviews was conducted following the ANDJ checklist. PubMed and Scopus were searched for systematic, scoping, and narrative reviews addressing assistive technologies relevant to tetraplegia. After screening, de-duplication, and quality appraisal, 20 reviews were included and synthesized narratively. Results: The included reviews clustered into four main domains: neural and regenerative interfaces, motor and biomechanical assistive systems, digital and adaptive rehabilitation ecosystems, and psychosocial and integrative frameworks. Across domains, evidence highlights a shift toward personalized, adaptive, and interoperable systems, supported by neurotechnologies, robotics, mobile health, and virtual reality. Common limitations include heterogeneous outcome measures, scarcity of longitudinal evidence, limited system interoperability, and persistent inequities in access and adoption. Emerging applications of artificial intelligence support adaptive control, monitoring, and personalization, though robust clinical validation remains limited. Conclusions: This synthesis provides a structured overview of review-level evidence on assistive technologies for tetraplegia. The findings underscore the need for coordinated, multidisciplinary approaches and more rigorous, longitudinal evaluation to support the development of inclusive, human-centered, and interoperable assistive ecosystems. Full article