Mapping Artificial Intelligence Research in Oral and Maxillofacial Surgery: A Bibliometric Analysis.

This study aimed to identify the role of artificial intelligence applications in enhancing future-foreseeing skills among early childhood teachers.The study adopted the descriptive analytical method, and data was collected using a questionnaire administered to a sample of 120 early childhood teachers in Al-Qurayyat Governorate.The results of the "first axis" showed that teachers have a positive vision regarding future foresight for the axis, with a high mean of (4.2).The phrase "I constantly develop my skills to keep pace with future changes" ranked first, followed by "I can adapt to technological changes in the field of education."In the "second axis," the results showed that teachers use technology to enhance their skills for the axis, with a high mean of (4.3).The phrase "Artificial intelligence helps save the teacher's time" ranked first, followed by "I use artificial intelligence applications to develop innovative study plans."In the "third axis," the results for the axis showed that teachers face challenges in using artificial intelligence applications with a high mean of (4.3).The phrase "There is a lack of educational resources related to artificial intelligence" ranked first, followed by "The technical infrastructure is insufficient to support the use of artificial intelligence."In the "fourth axis," the results showed a high approval rate for the proposals for the axis, with a high mean of (4.4).The phrase "Providing continuous training programs on the use of artificial intelligence" ranked first, followed by "Enhancing cooperation between educational and technical institutions will contribute to the development of integrated educational curricula that include artificial intelligence applications."Based on these results, a proposed vision was developed aimed at improving the quality of education by developing teachers' skills and empowering them to effectively face future challenges.

Achieving sustainable development with the application of artificial intelligence: Effects on global green energy security amid geopolitical risk

Artificial intelligence applications in urban extreme heat management: A systematic review of forecasting, monitoring, mitigation and decision support

A systematic review of the role of wearable devices and artificial intelligence applications in assessing functional outcomes after lumbar fusion.

Artificial intelligence applications and innovation ambidexterity: The “inverted U-shaped” regulating effect of risk-taking

Screening Tools and Strategies for Early Detection of Oral Cancer and Potentially Malignant Disorders in Rural and Low-Resource Populations: A Systematic Review.

Pullulan-based semi-interpenetrating network hydrogel sensor for artificial intelligence-driven pressure recognition.

Personalized weight loss management through wearable devices and artificial intelligence.

The rise of algorithmic governance and the dual revolution: Applications, challenges, and governance of artificial intelligence in public administration

This paper conducts a bibliometric and content analysis and offers a comprehensive overview of the research landscape on access to finance, revealing critical insights into the determinants of financial access, the role of financial intermediaries, and the impact of regulatory frameworks and providing potential future directions for FinTech innovations in the context of globalization. This study conducts a systematic literature review and bibliometric analysis spanning from 1950 to 2024, examining 2,657 relevant documents. The findings reveal three main clusters: access to finance, financial inclusion, and financial constraints. Financial access is essential for reducing poverty, fostering financial inclusion, and influencing environmental outcomes and digital integration. Financial constraints significantly impact investment behavior, innovation, and economic development, highlighting the importance of government policies and FinTech solutions. This review suggests several future research directions, including exploring FinTech innovations, sustainability criteria in financial inclusion, Artificial General Intelligence (AGI) applications in evaluating financial constraints, and understanding regional disparities in financial access and inclusion. These insights provide valuable guidance for policymakers and researchers to inform policy design, developing financial inclusion programs, and foster global economic growth and opportunities in globalization scope. This study contributes to the relevant literature by providing deeper insights into the themes and findings identified within each strand of this research field. The study highlights the importance of tangible assets and strong financial performance in improving access to finance. This study explores the important role of financial intermediaries in facilitating access to finance and the role of alternative financing options in enhancing financial inclusion and FinTech innovations. The study provides a deeper understanding of the impact of regulatory frameworks on access to finance by ensuring financial stability, enhancing transparency, and encouraging FinTech innovations in the globalization context.

Emerging Technologies, Education, and Governance in Latin American Dentistry: Perspectives from Venezuela and Nicaragua.

Artificial intelligence in emergency skeletal X-ray: post-deployment monitoring and clinical impact of incorrect AI results.

Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by the rapid proliferation of immature white blood cells in the bone marrow. Early and accurate diagnosis is essential for improving clinical outcomes; however, distinguishing between lymphocytes and lymphoblasts poses significant challenges owing to their subtle morphological similarities. Traditional manual diagnostic methods, which rely on expert evaluations, are inherently time-consuming and subject to human error. In recent years, machine learning and deep learning approaches have emerged as promising tools for automating and enhancing diagnostic processes. This review systematically examines state-of-the-art traditional and deep learning techniques applied for ALL detection and classification. We provide a comprehensive analysis of various methodologies, including supervised machine learning algorithms and advanced deep learning architectures, with a focus on critical stages such as image preprocessing, feature extraction, and blast cell quantification. Furthermore, we discuss the performance metrics and accuracy benchmarks, highlighting the potential of these techniques to match or exceed human diagnostic capabilities. The review concludes with a discussion of the current challenges, recent developments, and future directions in the application of artificial intelligence for ALL diagnosis, underscoring the need for continued innovation to meet emerging clinical demands.

Does the application of artificial intelligence enhance the labor income share? — Evidence from China’s listed companies

This study explores the application of artificial intelligence (AI) and geospatial tools to design 15-min cities, focusing on proximity, public space quality, environmental resilience, and inclusion. Using the post-industrial Young City district in Gdańsk, Poland, as a case study, the research integrates semantic segmentation (DeepLabv3) and AI-driven object detection for evaluating public space quality, flood simulation modeling (InVEST UFRM) for climate resilience, and network analysis using adjusted walking speeds (2.95 km/h) to capture the needs of vulnerable populations. By identifying “hotspots” with compounded deficits in access to urban amenities, public space quality, and resilience, the study provides a replicable, data-informed tool for guiding equitable urban transformation. The findings reveal critical spatial and environmental disparities, particularly in former fence-line neighborhoods in Gdańsk, which remain underserved despite ongoing urban redevelopment. The proposed framework offers practical insights for mainstreaming inclusive adaptation strategies into planning policies and contributes to the evolving discourse on operationalizing the 15-min city (FMC) concept in complex contexts. • Introduces an enhanced 15-minute city (FMC) framework combining spatial quality, environmental resilience, and inclusivity. • Applies advanced geospatial tools, including DeepLabv3 (segmentation) and InVEST flood modeling, for scalable urban analysis. • Reveals hotspots where accessibility, livability, and climate resilience deficits overlap, mainly in former fence-line zones. • Demonstrates a replicable methodology that bridges proximity metrics with public space quality and climate adaptation. • Supports inclusive urban transformation by noting the need for equitable investment in historically marginalized areas.

Artificial intelligence in microscopic hair imaging for scalp disorders: From image acquisition to clinical decisions.

Intelligent technologies in operating room nursing: A bibliometric analysis of research.

Ketogenic Diet Therapy (KDT) is an effective but complex treatment for paediatric drug-resistant epilepsy. Access to trained dietitians limits the global use of KDT. The increasing use of artificial intelligence (AI) chatbots for health and dietary advice presents both opportunities and risks. This exploratory study evaluates the accuracy and feasibility of AI-generated KDT plans for paediatric epilepsy. We conducted a comparative analysis of three major AI chatbots: ChatGPT, Microsoft Copilot, and Gemini. Three clinical vignettes, one for each KDT type, were created to represent common KDT diet plans: the Classic Ketogenic Diet (Classic KD), the Medium-Chain Triglyceride (MCT) diet, and the Modified Atkins Diet (MAD). Two KDT dietitians evaluated the AI-generated diet plans for accuracy of macronutrient calculations, appropriateness of food choices, and overall safety and feasibility. All three chatbots failed to produce accurate KD plans. In the Classic KD, none of the chatbots generated menus that matched their calculated macronutrient targets, resulting in widely varying and inaccurate ketogenic ratios. For the MCT diet, the generated menus provided significantly less MCT than prescribed and failed to meet the caloric and macronutrient goals. For the MAD, the chatbots exceeded the specified net carbohydrate limits and provided inappropriate food suggestions. AI chatbots, in their current state, are not reliable or safe for generating KDT plans for children with epilepsy. The inaccuracies in macronutrient calculations and inappropriate food suggestions pose significant risks. This study underscores the critical need for human oversight by qualified professionals in the clinical application of AI for specialised dietary therapies.

Artificial intelligence-based prediction of boar reproductive fitness and health: Current status in research and practice.