Search Results (452)

In the context of the rapid digitalization of higher education, proactive artificial intelligence (AI) agents embedded within multi-agent systems (MAS) offer new opportunities for personalized learning, improved quality of education, and alignment with sustainable development goals. This study aims to analyze how such AI solutions are perceived by students at Narxoz University (Kazakhstan) prior to their practical implementation. The research focuses on four key aspects: the level of student trust in AI agents, perceived educational value, concerns related to privacy and autonomy, and individual readiness to use MAS tools. The article also explores how these solutions align with the Sustainable Development Goals—specifically SDG 4 (“Quality Education”) and SDG 8 (“Decent Work and Economic Growth”)—through the development of digital competencies and more equitable access to education. Methodologically, the study combines a bibliometric literature analysis, a theoretical review of pedagogical and technological MAS concepts, and a quantitative survey (n = 150) of students. The results reveal a high level of student interest in AI agents and a general readiness to use them, although this is tempered by moderate trust and significant ethical concerns. The findings suggest that the successful integration of AI into educational environments requires a strategic approach from university leadership, including change management, trust-building, and staff development. Thus, MAS technologies are viewed not only as technical innovations but also as managerial advancements that contribute to the creation of a sustainable, human-centered digital pedagogy. Full article

This study aims to develop a performance evaluation system specifically for the design phase of high-rise building projects within the architecture, engineering, and construction industry, where performance is often only measured during construction. The research process included three stages: identification of 21 key performance indicators through a literature review and expert validation; development of standardized indicator sheets detailing calculation protocols and data collection procedures; and creation of a functional dashboard-based evaluation system using Excel. The system was validated through expert review and tested with a simulated project generated using an AI-based language model. The evaluation system proved functional, accessible, and effective in detecting performance issues across five core categories: planning, cost, time, quality, and people. The results from the simulated application highlighted strengths in quality and stakeholder collaboration but also revealed significant gaps in cost and time performance. This study addresses a gap in the existing literature by focusing on performance evaluation during the design phase of construction projects, a stage often underrepresented in performance studies. The resulting system offers a structured, practical tool adaptable to real-world projects. The validation relied on a limited number of expert participants and a simulated project. Future research should recommend broader international validation and real-world application. Full article

Background: Artificial intelligence (AI) is reshaping neuroimaging workflows for Alzheimer’s disease (AD) diagnosis, particularly through PET and MRI analysis advances. Since the FDA approval of Tauvid, a PET tracer targeting tau pathology, there has been a notable increase in studies applying AI to neuroimaging data. This narrative review synthesizes recent, high-impact literature to highlight clinically relevant AI applications in AD imaging. Methods: This review examined peer-reviewed studies published between January 2020 and January 2025, focusing on the use of AI, including machine learning, deep learning, and hybrid models for diagnostic and prognostic tasks in AD using PET and/or MRI. Studies were identified through targeted PubMed, Scopus, and Embase searches, emphasizing methodological diversity and clinical relevance. Results: A total of 111 studies were categorized into five thematic areas: Image preprocessing and segmentation, diagnostic classification, prognosis and disease staging, multimodal data fusion, and emerging innovations. Deep learning models such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based architectures were widely employed by the research community in the field of AD. At the same time, several models reported strong diagnostic performance, but methodological challenges such as reproducibility, small sample sizes, and lack of external validation limit clinical translation. Trends in explainable AI, synthetic imaging, and integration of clinical biomarkers are also discussed. Conclusions: AI is rapidly advancing the field of AD imaging, offering tools for enhanced segmentation, staging, and early diagnosis. Multimodal approaches and biomarker-guided models show particular promise. However, future research must focus on reproducibility, interpretability, and standardized validation to bridge the gap between research and clinical practice. Full article

Background/Objectives: The integration of artificial intelligence (AI) into forensic science is expanding, yet its application in firearm injury diagnostics remains underexplored. This study investigates the diagnostic capabilities of ChatGPT-4 (February 2024 update) in classifying gunshot wounds, specifically distinguishing entrance from exit wounds, and evaluates its potential, limitations, and forensic applicability. Methods: ChatGPT-4 was tested using three datasets: (1) 36 firearm injury images from an external database, (2) 40 images of intact skin from the forensic archive of the University of Catania (negative control), and (3) 40 real-case firearm injury images from the same archive. The AI’s performance was assessed before and after machine learning (ML) training, with classification accuracy evaluated through descriptive and inferential statistics. Results: ChatGPT-4 demonstrated a statistically significant improvement in identifying entrance wounds post-ML training, with enhanced descriptive accuracy of morphological features. However, its performance in classifying exit wounds remained limited, reflecting challenges noted in forensic literature. The AI showed high accuracy (95%) in distinguishing intact skin from injuries in the negative control analysis. A lack of standardized datasets and contextual forensic information contributed to misclassification, particularly for exit wounds. Conclusions: While ChatGPT-4 is not yet a substitute for specialized forensic deep learning models, its iterative learning capacity and descriptive improvements suggest potential as a supplementary diagnostic tool in forensic pathology. However, risks such as overconfident misclassifications and AI-generated hallucinations highlight the need for expert oversight and cautious integration in forensic workflows. Future research should prioritize dataset expansion, contextual data integration, and standardized validation protocols to enhance AI reliability in medico-legal diagnostics. Full article

Artificial intelligence (AI) is changing how we teach and learn, generating excitement and concern about its potential to transform education. To contribute to the debate, this systematic literature review examines current research trends (publication year, country of study, publication journal, education level, education field, and AI type), as well as the benefits and challenges of integrating AI into education. This review analyzed 155 peer-reviewed empirical studies published between 2015 and 2025. The review reveals a significant increase in research activity since 2022, reflecting the impact of generative AI tools, such as ChatGPT. Studies highlight a range of benefits, including enhanced learning outcomes, personalized instruction, and increased student motivation. However, there are challenges to overcome, such as students’ ethical use of AI, teachers’ resistance to using AI systems, and the digital dependency these systems can generate. These findings show AI’s potential to enhance education; however, its success depends on careful implementation and collaboration among educators, researchers, and policymakers to ensure meaningful and equitable outcomes. Full article

Large language models (LLMs) rapidly transform healthcare by automating tasks, streamlining administration, and enhancing clinical decision support. This rapid review assesses current and emerging applications of LLMs in diagnostic-related group (DRG) assignment and clinical decision support systems (CDSS), with emphasis on radiology and nuclear medicine. Evidence shows that LLMs, particularly those tailored for medical domains, improve efficiency and accuracy in DRG coding and radiology report generation, providing clinicians with actionable, context-sensitive insights by integrating diverse data sources. Advances like retrieval-augmented generation and multimodal architecture further increase reliability and minimize incorrect or misleading results that AI models generate, a term that is known as hallucination. Despite these benefits, challenges remain regarding safety, explainability, bias, and regulatory compliance, necessitating ongoing validation and oversight. The review prioritizes recent, peer-reviewed literature on radiology and nuclear medicine to provide a practical synthesis for clinicians, administrators, and researchers. While LLMs show strong promise for enhancing DRG assignment and radiological decision-making, their integration into clinical workflows requires careful management. Ongoing technological advances and emerging evidence may quickly change the landscape, so findings should be interpreted in context. This review offers a timely overview of the evolving role of LLMs while recognizing the need for continuous re-evaluation. Full article

Biodegradable polyesters have gained attention due to their sustainability benefits, considering the escalating environmental challenges posed by synthetic polymers. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), are expected to significantly accelerate research in polymer science. This review article explores “bio” polymer informatics by harnessing insights from the AI techniques used to predict structure–property relationships and to optimize the synthesis of bioplastics. This review also discusses PolyID, a machine learning-based tool that employs message-passing graph neural networks to provide a framework capable of accelerating the discovery of bioplastics. An extensive literature review is conducted on explainable AI (XAI) and generative AI techniques, as well as on benchmarking data repositories in polymer science. The current state-of-the art in ML methods for ring-opening polymerizations and the synthesizability of biodegradable polyesters is also presented. This review offers an in-depth insight and comprehensive knowledge of current AI-based models for polymerizations, molecular descriptors, structure–property relationships, predictive modeling, and open-source benchmarked datasets for sustainable polymers. This study serves as a reference and provides critical insights into the capabilities of AI for the accelerated design and discovery of green polymers aimed at achieving a sustainable future. Full article

This study investigates students’ perceptions of Generative Artificial Intelligence (GenAI), with a focus on Higher Education institutions in Northern Ireland and India. We collect quantitative Likert ratings and qualitative comments from 1211 students on their awareness and perceptions of AI and investigate variations in attitudes toward AI across institutions and subject areas, as well as interactions between these variables with demographic variables (focusing on gender). We found the following: (a) while perceptions varied across institutions, responses for Computer Sciences students were similar, both in terms of topics and degree of positivity; and (b) after controlling for institution and subject area, we observed no effect of gender. These results are consistent with previous studies, which find that students’ perceptions are predicted by prior experience; crucially, however, the results of this study contribute to the literature by identifying important interactions between key factors that can influence experience, revealing a more nuanced picture of students’ perceptions and the role of experience. We consider the implications of these relations, and further considerations for the role of experience. Full article

Tan spot disease, caused by Pyrenophora tritici-repentis, poses a significant threat to wheat production worldwide. Early detection and precise fungicide application are essential for effective disease management. This study explores the potential of Raman spectroscopy—specifically surface-enhanced Raman spectroscopy (SERS) and coherent anti-Stokes Raman scattering (CARS)—as non-invasive tools for identifying fungal infection and assessing wheat’s biochemical response to propiconazole treatment. The methodology is entirely theoretical; no laboratory experiments were conducted. Instead, all spectral graphs and figures were generated through a collaborative process between the author and Microsoft Copilot, which served as a rendering tool. These AI-assisted visualizations simulate Raman responses based on known molecular interactions and literature data. The results demonstrate the conceptual feasibility of Raman-based diagnostics for precision agriculture, offering a sustainable approach to disease monitoring and fungicide management. Full article

Background: The rapid shift from open to endovascular techniques in vascular surgery has significantly decreased trainee exposure to high-stakes open procedures. Simulation-based training, especially that incorporating virtual reality (VR) and artificial intelligence (AI), provides a promising way to bridge this skill gap. Objective: This narrative review aims to assess the current evidence on the integration of extended reality (XR) and AI into vascular surgeon training, focusing on technical skill development, performance evaluation, and educational results. Methods: We reviewed the literature on AI- and XR-enhanced surgical education across various specialties, focusing on validated cognitive learning theories, simulation methods, and procedure-specific training. This review covered studies on general, neurosurgical, orthopedic, and vascular procedures, along with recent systematic reviews and consensus statements. Results: VR-based training speeds up skill learning, reduces procedural mistakes, and enhances both technical and non-technical skills. AI-powered platforms provide real-time feedback, performance benchmarking, and objective skill evaluations. In vascular surgery, high-fidelity simulations have proven effective for training in carotid artery stenting, EVAR, rAAA management, and peripheral interventions. Patient-specific rehearsal, haptic feedback, and mixed-reality tools further improve realism and readiness. However, challenges like cost, data security, algorithmic bias, and the absence of long-term outcome data remain. Conclusions: XR and AI technologies are transforming vascular surgical education by providing scalable, evidence-based alternatives to traditional training methods. Future integration into curricula should focus on ethical use, thorough validation, and alignment with cognitive learning frameworks. A structured approach that combines VR, simulation, cadaver labs, and supervised practice may be the safest and most effective way to train the next generation of vascular surgeons. Full article

This systematic literature review explores recent advancements in polymer-based composite materials designed for thermal insulation in automotive applications, with a particular focus on sustainability, performance optimization, and scalability. The methodology follows PRISMA 2020 guidelines and includes a comprehensive bibliometric and thematic analysis of 229 peer-reviewed articles published over the past 15 years across major databases (Scopus, Web of Science, ScienceDirect, MDPI). The findings are structured around four central research questions addressing (1) the functional role of insulation in automotive systems; (2) criteria for selecting suitable polymer systems; (3) optimization strategies involving nanostructuring, self-healing, and additive manufacturing; and (4) future research directions involving smart polymers, bioinspired architectures, and AI-driven design. Results show that epoxy resins, polyurethane, silicones, and polymeric foams offer distinct advantages depending on the specific application, yet each presents trade-offs between thermal resistance, recyclability, processing complexity, and ecological impact. Comparative evaluation tables and bibliometric mapping (VOSviewer) reveal an emerging research trend toward hybrid systems that combine bio-based matrices with functional nanofillers. The study concludes that no single material system is universally optimal, but rather that tailored solutions integrating performance, sustainability, and cost-effectiveness are essential for next-generation automotive thermal insulation. Full article

Clinical trials in nephrology have historically been hindered by significant challenges, including slow disease progression, patient heterogeneity, and recruitment difficulties. While recent therapeutic breakthroughs have transformed care, they have also created a ‘paradox of success’ by lowering baseline event rates, further complicating traditional trial designs. We hypothesize that integrating innovative trial methodologies with advanced computational tools is essential for overcoming these hurdles and accelerating therapeutic development in kidney disease. This narrative review synthesizes the literature on persistent challenges in nephrology trials and explores methodological innovations. It investigates the transformative impact of computational tools, specifically Artificial Intelligence (AI), techniques like Augmented Reality (AR) and Conditional Tabular Generative Adversarial Networks (CTGANs), in silico clinical trials (ISCTs) and Digital Health Technologies across the research lifecycle. Key methodological innovations include adaptive designs, pragmatic trials, real-world evidence, and validated surrogate endpoints. AI offers transformative potential in optimizing trial design, accelerating patient stratification, and enabling complex data analysis, while AR can improve procedural accuracy, and CTGANs can augment scarce datasets. ISCTs provide complementary capabilities for simulating drug effects and optimizing designs using virtual patient cohorts. The future of clinical research in nephrology lies in the synergistic convergence of methodological and computational innovation. This integrated approach offers a pathway for conducting more efficient, precise, and patient-centric trials, provided that critical barriers related to data quality, model validation, regulatory acceptance, and ethical implementation are addressed. Full article

Background: Between 2014 and 2024, Poland underwent a significant digital transformation in its healthcare sector, evolving from isolated initiatives to a cohesive national eHealth ecosystem. This review examines the development, clinical significance, and research trends in telemedicine in Poland, providing comparative insights from 1995 to 2015 and assessing the impact of the COVID-19 pandemic. Methods: A narrative review was conducted using the PubMed, Scopus, EMBASE, and Web of Science databases to identify peer-reviewed articles published between January 2014 and December 2024. A total of 1012 records were identified, and 212 articles were included after applying predefined inclusion criteria. These articles were categorized by medical specialty, study type, COVID-19 relevance, and clinical versus nonclinical focus. Gray literature and policy reports were examined only to provide a context for the findings. Results: Ninety-six publications were included in the clinical studies. The most common specialties are cardiology, psychiatry, geriatrics, general practice, and rehabilitation. In earlier years, survey-based and observational designs were predominant, whereas later years saw an increase in interventional trials and studies enabled by Artificial Intelligence (AI). The COVID-19 pandemic has had a significant impact on research activity, accelerating the adoption of digital technologies in previously underrepresented fields, such as pulmonology and palliative care, as well as in the routine use of modern Internet communication technologies for daily patient–doctor interactions. Discussion: Advancements in digital health (including eHealth and telemedicine) in Poland have been driven by policy reforms, technological advancements, and epidemiological events, such as COVID-19. Various fields have evolved from feasibility studies to clinical trials, and emerging specialties have focused on user experience and implementation. However, the adoption of AI and its interoperability remains underdeveloped, primarily because of regulatory and reimbursement challenges. Conclusions: Poland has made significant strides in institutionalizing digital health; however, ongoing innovation necessitates regulatory alignment, strategic funding, and enhanced collaboration between academia and industry. As the country aligns with the European Union (EU) initiatives, such as the European Health Data Space, it has the potential to lead to regional integration in digital health. Full article

We present a novel hybrid approach to literature-based discovery (LBD) which exploits large language models (LLMs) to enhance traditional LBD methodologies. We explore the use of LLMs to address significant LBD challenges: (1) the extraction of factual subject–predicate–object relations from publication abstracts using few-shot learning and (2) the filtering of unpromising candidate hidden knowledge pairs (CHKPs) using a variant of the LLM-as-a-judge paradigm with and without the addition of domain-specific information using retrieval augmented generation. The approach produces relations with greater coverage and results in a lower number of CHKPs compared to LBD based on relations extracted with, e.g., SemRep, improving the prediction and efficiency of knowledge discovery. We demonstrate the utility of the method using a drug-repurposing case study and suggest that emerging AI technologies can be used to assist in knowledge discovery from the ever-growing volume of the scientific literature. Full article

Discrete-event simulation (DES) as an operations research (OR) technique has continued to evolve since its inception in the 1950s. DES evolution mirrors the advances in computer science (hardware and software, processing speed, and advanced information visualization capabilities). DES overcame the initial usability obstacles and lack of efficacy challenges in the early 2000s to remain a popular OR tool of “last resort.” Using bibliographic data from SCOPUS, this study undertakes a science mapping of the DES literature and evaluates its evolution and expansion in the past fifteen years. The results show asymmetrical but positive yearly literature output; broadened DES adoption in diverse fields; and sustained relevance as a potent OR method for tackling old, new, and emerging operations and production issues. The thematic analysis identifies DES as an essential tool that integrates and enhances digital twin technology in Industry 4.0, playing a central role in enabling digital transformation processes that have swept the industrial space in manufacturing, logistics, healthcare, and other sectors. DES integration with generative/artificial intelligence (GenAI/AI) provides a great potential to revolutionize modeling and simulation activities, tasks, and processes. Future studies will explore more ways to integrate GenAI tools in DES. Full article