Search Results (895)

Mobile health (mHealth) apps increasingly capture the physiological, biomechanical, and psychological variables involved in sport training, but the evidence remains fragmented across single-domain reviews, leaving practitioners without a consolidated basis for selecting and deploying these tools across the training process. This scoping review aimed to identify and characterize research on mHealth apps in sport training, focusing on their performance testing, training load and recovery monitoring, technical and skill development, injury screening and prevention, and athlete self-management. It also synthesized evidence regarding their applications, intended purposes, technical characteristics, and the evidence supporting their effectiveness. Five databases (PubMed, Scopus, Web of Science, SPORTDiscus, Embase) were searched from inception to July 2026 for journal articles reporting original empirical data on app research on the sport training process in athletes. Studies involving only the promotion of physical activity or lacking human-subject testing, including commercially available apps without supporting research on their effectiveness, were excluded. Findings were synthesized narratively, and methodological quality was appraised with the Mixed Methods Appraisal Tool. Of 9476 records identified, 111 studies met the inclusion criteria and were inductively classified into ten application categories: sport skill training (n = 26), performance measurement (n = 20), vertical jump measurement (n = 18), self-reported monitoring (n = 12), physiological measurement (n = 12), musculoskeletal screening (n = 10), psychological intervention (n = 5), nutrition (n = 3), anthropometric and maturation screening (n = 3), and tactical and match analysis (n = 2). Most apps relied on built-in smartphone sensors or no sensing at all and used manual or deterministic computation; processing location went unreported in 74.8% of studies, which reflects a reporting gap rather than an architectural profile of the field, and reported that AI or machine learning labels did not track with actual method disclosure. Validation and reliability designs dominated the evidence base (52%), while randomized or controlled effectiveness trials were rare (10%). Apps generally showed good relative validity but limited absolute accuracy against criterion instruments, and wherever apps were deployed longitudinally, adherence rather than accuracy determined their real-world value. mHealth apps now support nearly every stage of sport training and can substitute for laboratory instruments in select, validated use cases, including video-based sprint and jump timing and chest-strap-paired heart-rate variability monitoring. However, the field remains organized around demonstrating measurement accuracy rather than showing that app-guided decisions improve athlete outcomes. A successful pathway for mHealth app development should progress from technical validity, through measurement reliability and responsiveness, to decision rules, and then to practitioner adoption by coaches and athletes, ultimately yielding better athlete outcomes. Full article

Generative artificial intelligence (GenAI) has moved from an emerging educational tool to a structural challenge for science, technology, engineering, and mathematics (STEM) higher education. This narrative review argues that the most consequential effect of GenAI is not the automation of existing teaching practices but the need to redesign curricula, learning outcomes, pedagogies, and assessment around disciplinary judgment, critical verification, intellectual independence, and transparent, ethical use of GenAI. Its distinctive contribution is to frame GenAI as a problem of curriculum and assessment validity rather than primarily as a question of tool adoption or academic integrity. Because widely available systems can generate code, solve quantitative problems, summarize literature, draft laboratory reports, and produce fluent scientific prose, conventional submitted artifacts have become weaker indicators of the reasoning and competence they are intended to demonstrate. The review therefore examines the full programme-to-classroom pathway, connecting definitions of graduate competence with course design, classroom and laboratory practice, assessment, feedback, faculty capability, technology adoption, and iterative evaluation. The analysis integrates cognitive load theory, constructive alignment, constructivist perspectives, and frameworks of faculty capability and technology adoption. The biological sciences serve as a recurring disciplinary case because they combine conceptual knowledge, laboratory practice, computational analysis, and ethical decision-making, and are also being transformed by AI-based scientific methods. A worked cell biology example, structured using the Analysis, Design, Development, Implementation, and Evaluation model, operationalizes the review’s conceptual argument and demonstrates how GenAI integration can translate into needs analysis, outcome specification, resource development, blended laboratory implementation, assessment, and iterative redesign. The resulting design logic is generalized into a transferable five-step template for STEM curriculum redesign, with recommendations at programme, course, and institutional levels. Full article

In contemporary Information Technology (IT) enterprises, workforce role configuration decisions sit at the intersection of leadership strategy, corporate Artificial Intelligence (AI) transformation and sustainable operational governance. This study investigates how heterogeneous workforce role configurations affect Service Level Agreement (SLA) compliance in SLA-aware Business Process Model and Notation (BPMN) IT business processes, framing this as a people management and leadership decision problem with explicit Environmental, Social and Governance (ESG)-oriented trade-offs. A validated MATLAB Simulink simulation tool is employed to conduct structured scenario testing across combinations of human role configurations and AI maturity levels, measuring their impact on key Service Level Objectives (SLOs) and Key Performance Indicators (KPIs). To support leadership decision making, a Workforce Sustainability Index (WSI) is introduced that integrates SLA compliance with workforce cost and AI dependency risk, where AI dependency risk captures the social dimension of ESG by emphasizing workforce skill development, upskilling and reskilling pathways, human oversight and responsible AI adoption. The simulation results reveal that no universally optimal configuration exists; the best-performing workforce design depends on organizational context and leadership priorities, as captured through scenario-based weight configurations representing performance-driven, cost-driven, human-centric and balanced governance orientations. These findings provide IT leaders and organizational decision-makers with actionable, evidence-based guidance for sustainable workforce design in the context of corporate AI transformation, contributing to the broader discourse on people management, merit-based organizational performance, responsible AI governance and sustainable digital operations management. Full article

Generative artificial intelligence has been rapidly adopted by university students, yet there is limited evidence describing how and why medical students use it, particularly in regional settings. This cross-sectional study examined the use of generative artificial intelligence for learning and assessment among medical students at James Cook University, a regional Australian medical school with three North Queensland campuses. All enrolled students (Years 1–6) were invited to complete a 24-item online survey; closed-ended items were analysed using frequency and bivariate analyses by year level and gender, with correction for multiple comparisons, and open-ended responses were analysed using qualitative content analysis. In total, 438 students responded. Eighty percent reported using artificial intelligence for their studies or assignments, and 95% supported its use in medical education in some capacity. The most common uses were explaining concepts (56%) and answering medical content questions (54%). Pre-clinical students reported greater study-related use, whereas clinical year students reported greater assessment-related use. Male students also reported higher levels of use, willingness to pay, and trust in AI tools. Although uptake was high, trust was moderate, and students expressed concerns about professionalism and critical thinking. Medical schools should provide explicit guidance on acceptable use, incorporate artificial intelligence literacy training and ethical use guidelines, and redesign assessment to protect the skills students perceive to be most at risk. Full article

Background/Objectives: The present study aimed to assess the understandability and actionability of Arabic text generated by a large language model for commonly searched Arabic queries on dry mouth. Methods: Using Google Trends, the top 10 searches worldwide related to ‘oral dryness’ were entered in OpenAI’s Generative Pretrained Transformer 5.1. Generated texts were achieved independently. Assessments were performed using the Patient Education Materials Assessment Tool (PEMAT) to evaluate the content, word choice, and style. Results: Causes, symptoms, and treatment of dry mouth were the most common dry-mouth-related queries. The highly temporal distribution of search interests among Arabic-speaking countries peaked between 2020 and 2021, then remained high throughout 2023, before declining in November 2025. The mean PEMAT understandability and actionability scores were 89% and 80%, respectively. It was notable that all generated responses lacked visual aids, which could have made the content difficult to understand and insufficient for acting on the information. Moreover, the formal Arabic form of ‘causes of dry mouth’ with a glottal stop yielded lower actionability scores (60%) than the informal Arabic form (80%). Conclusions: Clinicians could actively supplement clinic-based discussions with advice on using large language models to help patients recognise dry mouth symptoms and improve self-care. Also, they could improve their effective adoption by clearly explaining expectations, limitations, and language/cultural differences when adopting these models. Full article

The diffusion of generative artificial intelligence has moved educational debate beyond access to new tools, placing teacher work, pedagogical mediation, assessment, and digital competence under renewed pressure. This article examines teachers’ perceptions, declared uses, and forms of engagement with generative AI in Gymnasien (academic-track secondary schools) and Gesamtschulen (comprehensive secondary schools) in North Rhine-Westphalia, Germany. The study is based on an anonymous and voluntary online questionnaire circulated through school leaderships. The operational database comprised 204 records, while the main analysis was based on 156 completed questionnaires. Closed-item responses were examined through descriptive statistics and interpreted through DigCompEdu and its AI-related supplement. The findings indicate an asymmetrical incorporation of generative AI across teachers’ work. Its presence is stronger in preparation, organisation, professional reflection, and risk awareness than in direct classroom mediation, process-oriented assessment, and institutionally supported student use. The strongest contrasts concern perceived student overreliance on AI-generated answers, the need to adapt assessment practices, limited school-level guidance, and weak use of AI-supported process monitoring. Rather than interpreting this imbalance as a simple delay in adoption, the article argues that teacher competence in relation to generative AI is produced through the interaction between professional judgement, pedagogical purposes, and institutional conditions. It therefore cannot be reduced to an individual technical attribute. Full article

Osteonecrosis of the femoral head (ONFH) is a progressive and potentially disabling condition caused by compromised blood supply to the femoral head, leading to bone necrosis and collapse. Early diagnosis is essential to enable joint-preserving interventions and improve patient outcomes. Magnetic resonance imaging (MRI) is currently considered the most sensitive modality for early detection, whereas computed tomography (CT) provides superior assessment of subchondral bone integrity and structural collapse. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques, has emerged as a promising tool to enhance diagnostic accuracy, automate lesion segmentation, and predict disease progression. This review aims to provide an overview of current AI applications in MRI for ONFH, focusing on early diagnostic, disease staging and classification, volumetric assessment, differential diagnosis and prognostic prediction. A total of 61 articles were initially identified, of which 13 studies (2021–2025) met the inclusion criteria. Results indicate that DL models, particularly convolutional neural networks (CNNs), achieve excellent diagnostic performance, with reported accuracies up to 98.4% and area under the curve (AUC) values reaching 0.98 for early-stage detection. Several models demonstrated performance comparable to or exceeding that of experienced clinicians, particularly in differentiating ONFH from other hip pathologies and in early disease recognition. AI algorithms also showed high accuracy in staging and classification (AUC up to 99.7% in internal validation), as well as in automated segmentation and volumetric assessment (Dice coefficients up to 0.89), enabling objective quantification of necrotic lesions. Furthermore, prognostic models integrating radiomics and ML techniques demonstrated promising results in predicting femoral head collapse (AUC up to 0.85). From a clinical perspective, AI appears to function primarily as a supportive tool, improving diagnostic consistency, efficiency, and reproducibility, and acting as a “second reader” capable of reducing variability among less experienced clinicians. However, significant limitations remain, including dataset heterogeneity, predominance of retrospective and monocentric studies, and limited integration of clinical data. In conclusion, AI-based MRI analysis shows strong potential to enhance the diagnosis, staging, and management of ONFH. Future research should focus on multicenter prospective validation, integration of multimodal clinical data, and development of explainable and generalizable models to facilitate widespread clinical adoption. Full article

Internet of Things (IoT) application systems are increasingly adopted in factories, shops, offices, and governments. However, building such systems using various devices and modules remains difficult for non-experts, because they must confirm specifications, communication interfaces, voltage ranges, and operating conditions from technical datasheets before connecting devices. In previous studies, we have explored a generative AI-based answering tool for datasheet inquiry using Retrieval-Augmented Generation (RAG) for technical guidance of IoT application system deployment. However, the adopted top-kRAG pipeline often retrieves multiple related text chunks, which can cause the AI to confuse technically different specifications, such as power output voltage, signal output voltage, and input voltage range, and produce inaccurate answers. In addition, the AI may generate a hallucinated answer if the datasheet does not provide sufficient source information. In this paper, we propose an AI answer-validation method using agentic RAG for datasheet inquiry for IoT application system deployment. The method organizes datasheet information into structured specification data, including device models, field types, values, units, conditions, and source information. For question-answering, the agent coordinates structured fact query, top-k text retrieval, source checking, and rule-based compatibility comparison according to the question type. Instead of fully relying on the LLM to interpret retrieved chunks, this method adopts structured specifications and deterministic source checks before accepting the final answer. For evaluation, we constructed a dataset from 20 IoT datasheets, including 1000 question-answering tasks with three difficulty levels. Compared with conventional top-k RAG, the proposed method improved the correct answer rate from 0.686 to 0.958 for easy questions, from 0.549 to 0.969 for medium questions, and from 0.273 to 0.613 for hard questions, which confirms the effectiveness of the proposed method. Full article

AI-generated reviews have become a core tool for hotel booking platforms in assisting user decision-making, yet existing research has not systematically explored the emotional tendencies of GAI reviews or the potential mechanisms by which temporal disclosure influences user decisions. This study integrates the Information Adoption Model (IAM) and the Elaboration Likelihood Model (ELM), incorporates the affordance perspective of GAI, employs a 2 × 2 experimental design (temporal disclosure × review valence), analyzes sample data from 649 travelers, and conducts empirical analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results demonstrate that temporal disclosure significantly enhances cognitive involvement; the effect of review valence on cognitive involvement is moderated by GAI affordance—high GAI affordance mitigates the inhibitory impact of positive reviews on cognitive involvement; empathy toward GAI information does not directly reduce psychological distance to hotels but operates indirectly through full mediation via psychological distance to GAI; psychological distance to GAI also indirectly influences booking intent via full mediation through psychological distance to the hotel. This study elucidates the mechanisms linking the emotional characteristics of AI-generated reviews with the impact of temporal disclosure on traveler decision-making, supplements theoretical frameworks for consumer decision-making in AI contexts, expands the IAM for AI applications, and provides empirical evidence and practical recommendations for optimizing GAI review presentation strategies on booking platforms. Full article

As artificial intelligence (AI) technologies advance, traditional craftsmen face new challenges: innovating using digital tools while preserving cultural authenticity and heritage knowledge. The “hybrid artisan,” who strategically integrates AI-based design tools with traditional craft, emerges as a response to this tension. This article addresses research questions regarding how integrating generative AI technologies into design processes influences: (1) artisans’ productivity and product quality; (2) cultural authenticity and heritage preservation; (3) sustainable business models in creative entrepreneurship. The research methodology employs a convergent design with mixed methods, combining: (a) a systematic literature review (SLR) guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA 2020, n = 33 articles, 2022–2025); and (b) a qualitative survey (n = 13 artisans, Romania; semi-structured questionnaire, 34 items). The literature review identifies three dominant human–AI collaboration models: task-level cooperation, process-level coordination, and system-level co-creation. Diffusion models fine-tuned with low-rank adaptation (LoRA) and generative adversarial networks (GANs) achieve cultural authenticity scores of 73–95% while reducing design time by 30–70%. Empirical data reveal paradoxes: artisans value authentic creativity and sustainability (4 of 13 respondents (31%) rate sustainability as “extremely important”) but adopt AI cautiously (6 of 13 respondents (46%) report that they were not familiar with AI tools). Those using AI report 15–40% productivity gains without a proportional increase in sales, suggesting that market recognition of AI-assisted crafts remains uneven and that sustainability benefits are not yet clearly linked to AI use in practice. The successful “hybrid artisan” model relies on collaborative rather than autonomous AI positioning, explicit cultural safeguards in system design, and transparent communication with consumers about AI involvement. This research provides a conceptual heuristic, points to new research directions, and outlines policy implications for understanding when and how AI-assisted craft practices may support cultural integrity while also accepting that such benefits are context-dependent and not universally validated. Full article

Endometrial cancer is one of the most rapidly increasing gynaecological malignancies worldwide. The clinically adapted molecular classification of endometrial carcinoma, derived from The Cancer Genome Atlas, comprises four major subtypes: POLE-mutated, mismatch repair-deficient, p53-abnormal expression, and no specific molecular profile. Its clinical implementation has improved prognostic stratification, risk assessment, and treatment decision-making in patients with endometrial carcinoma. However, current workflows rely on immunohistochemistry and targeted sequencing, which increase costs, turnaround times, and infrastructure requirements, thereby limiting their universal adoption in routine clinical practice. Recent advances in artificial intelligence (AI), particularly deep learning models capable of predicting molecular features directly from H&E-stained whole-slide images, have emerged as promising tools for precision oncology. In addition to reproducing established molecular classification, these approaches may reveal previously unrecognised biomarker-defined histologic patterns that are difficult to detect using conventional methods. This article synthesises the current evidence on AI-based molecular classification in endometrial carcinoma from a pathologist-centred perspective, emphasising the biological rationale, methodological limitations, and future directions for clinical translation. Full article

The progressive integration of Whole Slide Imaging (WSI) technology and Artificial Intelligence (AI) architectures is driving a structural transformation in pathology and precision oncology. This structured critical review analyzes and systematizes the impact of this technological transition along two fundamental operational dimensions of the modern biopharmaceutical industry: pre-registration Clinical Research and post-launch strategies governed by Medical Affairs. The first section explores how computational pathology is improving efficiency and reducing risk in drug development. Replacing analog visual assessment—intrinsically subject to inter-observer and intra-observer variability—with quantitative algorithms for cellular classification and segmentation enables optimization of patient recruitment in clinical trials, reducing screening failure rates. This review also examines the emerging role of Spatial Biology in extracting complex topological metrics from the Tumor Microenvironment (TME) and the use of AI for the objective and auditable quantification of critical surrogate endpoints, such as Pathological Complete Response (pCR), while acknowledging that algorithmic precision remains sensitive to pre-analytical variables and dataset biases. In the second section, the study investigates the strategic evolution of Medical Affairs, acting as a vital scientific communication and translational bridge between the complexity of Data Science and clinical hospital practice. Challenges related to AI adoption by clinicians are examined, emphasizing the importance of educational programs based on Explainable AI (XAI) to overcome the cognitive limitations of the black-box paradigm and the complex regulatory validation pathway for Software as a Medical Device (SaMD) under the stringent European IVDR framework—supported by an analysis of historical regulatory benchmarks such as the Paige Prostate case. The paper also explores the potential of AI in the large-scale generation of Real-World Evidence (RWE), applied to the creation of synthetic control arms in pharmacoeconomic settings. In conclusion, the study highlights that the diagnostic algorithm has ceased to be merely a laboratory support tool and has become a strategic asset and an integral adjunct to therapeutic decision-making. Overcoming current challenges related to data privacy through Federated Learning architectures, together with the imminent transition toward Foundation Models, foreshadows a fully data-driven healthcare ecosystem, making continuous skills development (digital upskilling) an essential requirement for professionals in the biopharmaceutical sector. Full article

Construction supply chains (CSCs) are increasingly exposed to material shortages, demand fluctuations, logistics disruptions, and inter-organizational coordination failures. Artificial intelligence (AI) offers new opportunities to improve construction supply chain resilience (CSCR) by strengthening prediction, information processing, and collaborative decision-making. However, the mechanisms through which AI capabilities enhance CSCR remain insufficiently understood. Drawing on organizational information processing theory (OIPT) and dynamic capabilities theory (DCT), this study examines whether AI capabilities affect proactive and reactive CSCR directly or indirectly through three dimensions of supply chain integration (SCI): operational, information, and relational integration. It further compares the relative strengths of these pathways. This research adopts an explanatory sequential mixed-methods design. In the quantitative phase, 353 valid questionnaires from construction professionals in China were analyzed using partial least squares structural equation modeling (PLS-SEM). In the qualitative phase, semi-structured interviews with 15 experts, alongside three real-world cases, were utilized to interpret the quantitative findings and identify contextual boundary conditions. The results demonstrate that AI capabilities have significant positive effects on both proactive CSCR (β = 0.140, p < 0.01) and reactive CSCR (β = 0.116, p < 0.05). Furthermore, AI capabilities significantly promote operational integration (β = 0.299, p < 0.001), information integration (β = 0.361, p < 0.001), and relational integration (β = 0.227, p < 0.001), which in turn enhance both resilience dimensions. Notably, information integration is an important aspect of proactive resilience (β = 0.290, p < 0.001), while operational integration is crucial for reactive resilience (β = 0.274, p < 0.001). The qualitative findings further indicate that environmental uncertainty, technical readiness, and top management support condition the effectiveness of AI-enabled SCI. Theoretically, grounded in OIPT and DCT, this study clarifies the pathways through which AI affects CSCR and the contextual conditions shaping these effects, thereby advancing the analytical framework for AI-driven resilience. Practically, it delivers tiered implementation guidance for construction stakeholders to deploy AI tools for layered integration, thereby specifically enhancing both pre-disruption proactive risk prevention and post-shock reactive recovery capacities. Full article

Landscape Character Assessment (LCA) has struggled with scale mismatches and inadequate treatment of cultural expression when applied to high-density cultural landscapes in Asia. On the Chengdu Plain, Western Sichuan, China, remote sensing shows convergent settlement patterns across case sites, but fieldwork reveals substantial divergences in landscape character. We examined five cases representing different rural development models in the Chengdu metropolitan area. By integrating 10 m Sentinel-2 imagery and with systematic fieldwork (3200 photographs and 42 semi-structured interviews, July–August 2023), we constructed a framework that combines macro-scale spatial analysis with micro-scale field investigation. For macro-level quantification, six landscape pattern indices were adopted: number of patches (NP), patch density (PD), contagion index (CONTAG), aggregation index (AI), Shannon’s diversity index (SHDI), and Shannon’s evenness index (SHEI). The micro-level investigation followed a three-tier scheme (natural environment, settlement space, architectural details) to record vertical ecological communities, spatial expressions of cultural embeddedness, and visual landscape character. Indicator screening employed the KJ method and two rounds of Delphi consultation with ten experts; weights were then determined through the Analytic Hierarchy Process (AHP). Macro-level results display a clustered, low-fragmentation pattern (woodland AI = 97.48, water body AI = 99.40), anchored by a stable mosaic of Linpan, farmland, water bodies, and homesteads. Micro-level features include multi-strata vertical communities of trees, shrubs, grasses, and water, along with cultural embeddedness expressed through irrigation systems, farming patterns, and courtyard layouts. The VLEAS (Vernacular Landscape Elements Assessment System) framework encompasses three dimensions (natural, cultural, and visual) and comprises 12 indicators. The cultural dimension receives the highest weight (0.42), followed by natural (0.32) and visual (0.26) dimensions. Comparative analysis shows that the framework differentiates cases with similar macro-scale patterns but divergent micro-features, capturing differences that a purely macro-index benchmark misses, and it also reveals trade-offs among ecological integrity, cultural heritage, and visual quality. Sensitivity analysis displays that case rankings remain stable across four extreme weighting scenarios (cultural, ecological, visual, equal), with only one swap (Xingfu Pastoral Park and Nongke Village) under ecological priority and no rank change greater than one position, confirming that the results are not driven by any specific weight assignment. This approach extends the utility of LCA in high-density rural cultural landscapes and offers a practical tool for differentiated conservation and management, especially in tourism-influenced rural settings on the Chengdu Plain. Transferability to other regions would require recalibration of locally relevant indicators. Full article

Aortitis—inflammation of the aortic wall—presents at the interface of vasculitis, infection, structural aortic disease, and cardiovascular risk. It may occur in giant cell arteritis (GCA), Takayasu arteritis, immunoglobulin G4 (IgG4)-related disease, drug-induced injury, infection, or as an isolated finding after aortic surgery. Modern imaging detects aortic inflammation more frequently, but the main challenge is classification rather than detection: determining whether disease is infectious or immune-mediated, active or dominated by fixed structural damage, systemic or isolated, and whether the dominant threat is aneurysm, dissection, undertreated infection, or avoidable immunosuppression. This review considers aortitis as a high-risk vascular syndrome requiring aetiology-first classification rather than descriptive labelling. Before escalating immunosuppression, infection must be actively excluded and inflammatory activity distinguished from fixed vascular damage. Treatment should be individualised according to phenotype, age, vascular territory, comorbidity, and toxicity risk, with surveillance continuing even after symptoms and inflammatory markers improve. Optimal care depends on multidisciplinary assessment integrating rheumatology, infectious diseases, vascular surgery, radiology, and cardiology expertise. Progress will require standardised imaging definitions, registries linking inflammatory control with structural vascular outcomes, and validation of artificial intelligence (AI) tools before clinical adoption. Full article