Search

Purpose: Examination of the scientific literature on cholesteatoma from past to present using bibliometric methods. Methods: A total of 2353 articles on cholesteatoma between 1980 and 2023 were obtained from the Web of Science database and statistically analysed using bibliometric techniques. Results: The journals with the highest number of contributions to the literature were Otology & Neurotology (n = 192), Acta Oto-Laryngologica (156) and Laryngoscope (149). The most impactful journals based on h-index and total citation (TC) counts were Laryngoscope (h:36, TC: 4241), Otology & Neurotology (29, 3141), and American Journal of Otology (26, 2071) respectively. The most prolific author was Kojima H. (n = 49). According to the findings of the Reference Publication Year Spectroscopy (RPYS) analysis, there has been a significant increase in academic interest in Cholesteatoma since its first description in 1959, particularly following the expansion of its definition in 1977. Subsequently, the trend of increased interest has risen rapidly in later years, with notable peaks in research intensity observed in 1989, 2006, and 2011. The highest academic trend occurred in 2006. In recent years, trending topics have included innovative approaches such as diagnostic and imaging methods particularly alongside technological developments, endoscopic surgery, recurrence and complications, prognosis and quality of life, artificial intelligence, and obliteration. Conclusion: In conclusion, the evolution of cholesteatoma and its research priorities reveal temporal shifts and emerging areas of interest over time. Primarily, diagnosis, treatment, and surgical approaches remain central. Additionally, research has expanded to encompass developments in surgical techniques, imaging modalities, cellular biology, pathology, and molecular mechanisms. [ABSTRACT FROM AUTHOR]

Purpose: The management of renal cell carcinoma (RCC) relies on clinical and histopathological features for treatment decisions. Recently, radiomics, which involves the extraction and analysis of quantitative imaging features, has shown promise in improving RCC management. This review evaluates the current application and limitations of radiomics for predicting treatment and oncological outcomes in RCC. Methods: A systematic search was conducted in Medline, EMBASE, and Web of Science databases or studies that used radiomics to predict response to treatment and survival outcomes in patients with RCC. The study quality was assessed using the Radiomics Quality Score (RQS) tools. Results: The systematic review identified a total of 27 studies, examining 6,119 patients. The most used imaging modality was contrast-enhanced abdominal CT. The reviewed studies extracted between 19 and 3376 radiomics features, including Histogram, Texture, Filter, or transformation method. Radiomics-based risk stratification models provided valuable insights into treatment response and oncological outcomes. All developed signatures demonstrated at least modest accuracy (AUC range: 0.55–0.99). The studies included in this analysis reported heterogeneous results regarding radiomics methods. The range of Radiomics Quality Score (RQS) was from − 5 to 20, with a mean RQS total of 9.15 ± 7.95. Conclusion: Radiomics has emerged as a promising tool in the management of RCC. It offers the potential for improved risk stratification and response assessment. However, future trials must demonstrate the generalizability of findings to prospective cohorts before progressing towards clinical translation. [ABSTRACT FROM AUTHOR]

The rapid evolution of knowledge requires constantly acquiring and updating skills, making lifelong learning crucial. Despite decades of artificial intelligence, recent advances promote new solutions to personalize learning in this context. The purpose of this article is to explore the current state of research on the development of artificial intelligence-mediated solutions for the design of personalized learning paths. To achieve this, a systematic literature review (SRL) of 78 articles published between 2019 and 2024 from the Scopus and Web or Science databases was conducted, answering seven questions grouped into three themes: characteristics of the published research, context of the research, and type of solution analyzed. This study identified that: (a) the greatest production of scientific research on the topic is developed in China, India and the United States, (b) the focus is mainly directed towards the educational context at the higher education level with areas of opportunity for application in the work context, and (c) the development of adaptive learning technologies predominates; however, there is a growing interest in the application of generative language models. This article contributes to the growing interest and literature related to personalized learning under artificial intelligence mediated solutions that will serve as a basis for academic institutions and organizations to design programs under this model. [ABSTRACT FROM AUTHOR]

Computational thinking (CT) has received growing interest as a research subject in the last decade, with research contributions attempting to capitalize on the benefits that CT may provide. This study included a systematic analysis aimed at revealing current trends in the CT subject, identifying educational interventions, and emerging assessment instruments. It also gave an overview of how teachers learned CT skills and how they integrated the CT curriculum into classroom practices. We searched the data in the Web of Science database and identified 360 articles. Most importantly, it emphasized the following points: (a) the most popular subject areas in CT literature; (b) CT intervention tools; (c) CT assessment practices used so far within educational courses; and (d) effective CT approaches to influence pre-service teachers. Results from this review revealed that CT's promotion in education had achieved significant progress in recent years. Along with the growth in the number of CT studies, the number of subjects, research questions, and teaching approaches also increased in recent years. It was also found that CT was mostly used in science, mathematics, programming, and computer science tasks, with little work in artificial intelligence (AI) and non-STEM areas. The essence of this paper implicated the researchers in designing the curriculum based on different subject domains. Furthermore, we recommended integrating augmented reality-based games using CT methodologies into the curriculum. [ABSTRACT FROM AUTHOR]

Copyright of Aydin Dental Journal is the property of Aydin Dental Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Antibiotic resistance emerged shortly after the discovery of the first antibiotic and has remained a critical public health issue ever since. Managing antibiotic resistance in clinical settings continues to be challenging, particularly with the rise of superbugs, or bacteria resistant to multiple antibiotics, known as multidrug-resistant (MDR) bacteria. This rapid development of resistance has compelled researchers to continuously seek new antimicrobial agents to curb resistance, despite a shrinking pipeline of new drugs. Recently, the focus of antimicrobial discovery has shifted to plants, fungi, lichens, endophytes, and various marine sources, such as seaweeds, corals, and other microorganisms, due to their promising properties. For this review, an extensive search was conducted across multiple scientific databases, including PubMed, Elsevier, ResearchGate, Scopus, and Google Scholar, encompassing publications from 1929 to 2024. This review provides a concise overview of the mechanisms employed by bacteria to develop antibiotic resistance, followed by an in-depth exploration of plant secondary metabolites as a potential solution to MDR pathogens. In recent years, the interest in plant-based medicines has surged, driven by their advantageous properties. However, additional research is essential to fully understand the mechanisms of action and verify the safety of antimicrobial phytochemicals. Future prospects for enhancing the use of plant secondary metabolites in combating antibiotic-resistant pathogens will also be discussed. [ABSTRACT FROM AUTHOR]

The advent of artificial intelligence (AI) technologies has taken the world of science by storm in 2023. The opportunities of this easy to access technology for clinical pharmacy research are yet to be fully understood. The development of a custom-made large language model (LLM) (DELSTAR) trained on a wide range of internationally recognised scientific publication databases, pharmacovigilance sites and international product characteristics to help identify and summarise medication related information on delirium, as a proof-of-concept model, identified new facilitators and barriers for robust clinical pharmacy practice research. This technology holds great promise for the development of much more comprehensive prescribing guidelines, practice support applications for clinical pharmacy, increased patient and prescribing safety and resultant implications for healthcare costs. The challenge will be to ensure its methodologically robust use and the detailed and transparent verification of its information accuracy. [ABSTRACT FROM AUTHOR]

Keeping up with the hastily growing economy implies undergoing unremitting transformation permanently. In the field of supply chain, such progress can only be guaranteed via the exploration of new horizons and innovative solutions in response to the constraints of the global market. Emerging technologies, particularly artificial intelligence, offer promising avenues for enhancing supply chain processes, with sustainability ascending as a critical consideration. Despite the recent surfacing of AI-driven applications, scant attention has been devoted to exploring their full potential within supply chain operations, particularly in conjunction with SDGs. Recognizing the untapped opportunities presented by the implementation of AI for a sustainable supply chain this study undertakes a bibliometric analysis of 236 research papers sourced from the Web of science database. The analysis utilizes R language BiblioShiny to examine the extracted papers, and dissect patterns, trends, and relationships among key concepts and themes as well as prominent topics, impactful authors, and leading journals and countries in this domain. The findings reveal substantial growth in research related to SCM, AI, and sustainability as the UK leads this field of study with 132 articles followed by India, China and the USA. Eventually, the National University of Singapore came first in terms of paper affiliations, followed by De La Salle University, and London Metropolitan University. These results only prove that sustainability is becoming more critical in the equation of AI-driven supply chains especially with the current socio-political and economic circumstances, constituting a solid base for further academic research and more innovations in the managerial and business-related policies in this field. [ABSTRACT FROM AUTHOR]

The study of Large Language Models (LLMs), as an interdisciplinary discipline involving multiple fields such as computer science, artificial intelligence, and linguistics, has diverse collaborations within its field. In this study, papers related to LLMs in the SSCI and SCI sub-collections of the Web of Science core database from January 2020 to April 2024 are selected, and a mixed linear regression model is used to assess the impact of scientific collaborations on the application of LLMs. On this basis, the paper further considers factors such as financial support and dominant countries to deeply explore the heterogeneous impact of scientific collaborations on the application of LLMs. The findings show that (1) excessive involvement of academic institutions limits the research and application of LLMs, and the number of authors does not have a significant effect on the application of LLMs; (2) with or without financial support, the role played by scientific collaborations in the application of LLMs does not significantly change; and (3) differences in the dominant countries of scientific collaborations have a slightly heterogeneous effect on the role of LLMs applications, which are mainly reflected in the number of collaborators. [ABSTRACT FROM AUTHOR]

Trustworthiness is the most significant predictor of trust and has a significant impact on people's levels of trust. Most trustworthiness-related research is empirical, and while it has a long history, it is challenging for academics to get insights that are applicable to their fields of study and to successfully transfer fragmented results into practice. In order to grasp their dynamic development processes through the mapping of network knowledge graphs, this paper is based on the Web of Science database and uses CiteSpace (6.2.R4) software to compile and visualize the 1,463 publications on trustworthy studies over the past 10 years. This paper aims to provide valuable references to theoretical research and the practice of Trustworthiness. The findings demonstrate that: over the past 10 years, trustworthiness-related research has generally increased in volume; trustworthiness research is concentrated in industrialized Europe and America, with American research findings having a bigger global impact; The University of California System, Harvard University, and Yale University are among the high-production institutions; the leading figures are represented by Alexander Todorov, Marco Brambilla, Bastian Jaeger, and others; the core authors are distinguished university scholars; however, the level of cooperation of the core author needs to be improved. The primary journal for publishing research on trustworthiness is the Journal of Personality and Social Psychology and Biology Letters. In addition, the study focuses on three distinct domains, involving social perception, facial clues, and artificial intelligence. [ABSTRACT FROM AUTHOR]

Purpose: The overall aim of the presented paper was to identify the main areas of application of artificial intelligence in advertising activities. Design/methodology/approach: In order to realise the objectives of this paper, it was decided to use two research methods: bibliometric analysis and a multiple case study. First, a quantitative analysis of the resources of the Scopus and Web of Science databases was carried out, which was used to isolate the main research areas concerning the application of artificial intelligence in advertising. Next, examples of advertisements from the business world that use artificial intelligence were searched for and assigned to each research area. In the final phase, the case studies were related to the results of a qualitative analysis of the literature exported during bibliometric research. Findings: With capabilities such as analysing, automating, creating and optimising, AI systems can have a variety of applications in advertising. In particular, they are used to monitor trends, targeting, behavioural prediction, personalisation, creation, audience interaction and engagement. Research limitations/implications: The article contains a preliminary study. In the future it is planned to conduct additional quantitative and qualitative research. Practical implications: The conclusions of the study can serve to better understand the relevance of artificial intelligence for the advertising industry and the practical possibilities of its use. The results of the study can be used both in market practice and as inspiration for further research in this area. Originality/value: The article demonstrates the specificity of artificial intelligence in relation to promotion. The research uses examples from business practice. [ABSTRACT FROM AUTHOR]

The diversity of mobile devices and the increasing availability of technology solutions support the growth and popularity of mobile learning environments. The ubiquitous nature of mobile devices has revolutionized learning, fostering the rise of mobile learning (mlearning). In combination with AI applications, m-learning environments further increase the potential for providing individualized solutions. This literature review delves into the current landscape of AI-based m-learning research. The findings are expected to unveil the immense potential of AI in transforming mlearning. Artificial intelligence's capabilities to personalize the learning experience, optimize the assessment process, and automate content adaptation and creation provide the opportunity to make mlearning more engaging and interactive with the potential offered by learning anytime, anywhere. The aim of this systematic review is to assess the current situation regarding the coordinated use of mobile learning and artificial intelligence applications. Recent studies in the last five years (2019-2023) were analyzed in Web of Science and Scopus databases. The systematic review was conducted in accordance with the PRISMA guidelines, and the relevant studies were analyzed with MAXQDA 24. The results include trends in research on applications used, personalized learning experience, independent learning, performance assessment and feedback, interaction, theoretical frameworks. Suggestions for future research are discussed based on the research results. [ABSTRACT FROM AUTHOR]

The evolution of technology is giving rise to new scenarios in communication, information access, and social relations. Particularly, artificial intelligence has a great impact on the current media ecosystem, including social, academic, communicative, health aspects, and interpersonal relationships. This research aims to study how artificial intelligence is reflected in the scientific production in the most relevant publications in Social Sciences. To this end, a systematic review of the scientific literature published in Spanish on the Web of Science and Scopus databases spanning from 2018 to the first three quarters of 2023 was carried out, following the standards of PRISMA Statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). From an initial sample of 159 articles, 109 were analysed after applying the inclusion and exclusion criteria. Results show that 2022 was the most productive year, with Spain having the highest number of publications. Furthermore, most of the research was published on Scopus and in the field of Law, with a predominance of qualitative methodology. The key themes were the benefits of implenting artificial intelligence (AI) and its dangers and threats. [ABSTRACT FROM AUTHOR]

Since ChatGPT launch in November 2022, artificial intelligence has become more and more widespread in all areas of life. Generative applications of artificial intelligence are proliferating in a wide range of fields. The technology has great potential for applications such as machine translation, voice recognition, education, or content creation, but it also raises concerns about misuse, ethical use, and plagiarism. As texts generated by artificial intelligence tools continue to improve, detection tools on the market will have to involve additional efforts to keep pace. This article uses data from the Scopus and Web of Science databases to map the current usability of detectors, of texts generated by artificial intelligence, in higher education and academia. One of the aims of the article is to provide an insight into the experiences with currently available detectors of texts generated by artificial intelligence in higher education. [ABSTRACT FROM AUTHOR]

Retinitis pigmentosa (RP) is often undetected in its early stages. Artificial intelligence (AI) has emerged as a promising tool in medical diagnostics. Therefore, we conducted a systematic review and meta-analysis to evaluate the diagnostic accuracy of AI in detecting RP using various ophthalmic images. We conducted a systematic search on PubMed, Scopus, and Web of Science databases on December 31, 2022. We included studies in the English language that used any ophthalmic imaging modality, such as OCT or fundus photography, used any AI technologies, had at least an expert in ophthalmology as a reference standard, and proposed an AI algorithm able to distinguish between images with and without retinitis pigmentosa features. We considered the sensitivity, specificity, and area under the curve (AUC) as the main measures of accuracy. We had a total of 14 studies in the qualitative analysis and 10 studies in the quantitative analysis. In total, the studies included in the meta-analysis dealt with 920,162 images. Overall, AI showed an excellent performance in detecting RP with pooled sensitivity and specificity of 0.985 [95%CI: 0.948–0.996], 0.993 [95%CI: 0.982–0.997] respectively. The area under the receiver operating characteristic (AUROC), using a random-effect model, was calculated to be 0.999 [95%CI: 0.998–1.000; P < 0.001]. The Zhou and Dendukuri I² test revealed a low level of heterogeneity between the studies, with [I2 = 19.94%] for sensitivity and [I2 = 21.07%] for specificity. The bivariate I² [20.33%] also suggested a low degree of heterogeneity. We found evidence supporting the accuracy of AI in the detection of RP; however, the level of heterogeneity between the studies was low. [ABSTRACT FROM AUTHOR]

Copyright of Hachetetepé: Revista Científica de Educación y Comunicación is the property of Universidad de Cadiz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The integration of wireless sensors with artificial intelligence could represent a transformative approach in restorative dentistry, offering a sophisticated means to enhance diagnostic precision, treatment planning, and patient outcomes. This systematic review was conducted to pinpoint and assess the efficacy of wireless sensors in restorative dentistry. The search methodology followed the guidelines outlined by PRISMA and involved the utilization of prominent scientific databases. Following the final phase of evaluating eligibility, the systematic review included six papers. Five experiments were conducted in vitro, while one was a randomized clinical trial. The investigations focused on wireless sensors for cavity diagnosis, toothbrush forces, facial mask applications, and physiological parameter detection from dental implants. All wireless sensors demonstrated efficacy in achieving the objectives established by each study and showed the validity, accuracy, and reproducibility of this device. The investigations examined in this systematic review illustrate the potential of wireless sensors in restorative dentistry, especially in the areas of caries detection, dental implant systems, face masks, and power brushes. These technologies hold promise for enhancing patient outcomes and alleviating the workload of dental practitioners. [ABSTRACT FROM AUTHOR]

Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks. [ABSTRACT FROM AUTHOR]

This study aimed to comprehensively evaluate the current utilization and future potential of ChatGPT, an AI-based chat model, in the field of radiology. The primary focus is on its role in enhancing decision-making processes, optimizing workflow efficiency, and fostering interdisciplinary collaboration and teaching within healthcare. A systematic search was conducted in PubMed, EMBASE and Web of Science databases. Key aspects, such as its impact on complex decision-making, workflow enhancement and collaboration, were assessed. Limitations and challenges associated with ChatGPT implementation were also examined. Overall, six studies met the inclusion criteria and were included in our analysis. All studies were prospective in nature. A total of 551 chatGPT (version 3.0 to 4.0) assessment events were included in our analysis. Considering the generation of academic papers, ChatGPT was found to output data inaccuracies 80% of the time. When ChatGPT was asked questions regarding common interventional radiology procedures, it contained entirely incorrect information 45% of the time. ChatGPT was seen to better answer US board-style questions when lower order thinking was required (P = 0.002). Improvements were seen between chatGPT 3.5 and 4.0 in regard to imaging questions with accuracy rates of 61 versus 85%(P = 0.009). ChatGPT was observed to have an average translational ability score of 4.27/5 on the Likert scale regarding CT and MRI findings. ChatGPT demonstrates substantial potential to augment decision-making and optimizing workflow. While ChatGPT’s promise is evident, thorough evaluation and validation are imperative before widespread adoption in the field of radiology. [ABSTRACT FROM AUTHOR]

Purpose: This review was designed to compare different corneal imaging modalities using artificial intelligence (AI) for the diagnosis of keratoconus (KCN), subclinical KCN (SKCN), and forme fruste KCN (FFKCN). Methods: A comprehensive systematic search was conducted in scientific databases, including Web of Science, PubMed, Scopus, and Google Scholar based on the PRISMA statement. Two independent reviewers assessed all potential publications on AI and KCN up to March 2022. The Critical Appraisal Skills Program (CASP) 11-item checklist was used to evaluate the validity of the studies. Eligible articles were categorized into three groups (KCN, SKCN, and FFKCN) and included in the meta-analysis. The pooled estimate of accuracy (PEA) was calculated for all selected articles. Results: The initial search yielded 575 relevant publications, of which 36 met the CASP quality criteria and were included in the analysis. Qualitative assessment showed that Scheimpflug and Placido combined with biomechanical and wavefront evaluations improved KCN detection (PEA, 99.2, and 99.0, respectively). The Scheimpflug system (92.25 PEA, 95% CI, 94.76–97.51) and a combination of Scheimpflug and Placido (96.44 PEA, 95% CI, 93.13–98.19) had the highest diagnostic accuracy for the detection of SKCN and FFKCN, respectively. The meta-analysis outcomes showed no significant difference between the CASP score and accuracy of the publications (all P > 0.05). Conclusions: Simultaneous Scheimpflug and Placido corneal imaging methods provide high diagnostic accuracy for early detection of keratoconus. The use of AI models improves the discrimination of keratoconic eyes from normal corneas. [ABSTRACT FROM AUTHOR]

The new AI based conversational chatbot, ChatGPT, launched in November 2022, is causing a stir. There are many opinions about this being a ‘threat or a promise,’ and thus it is important to understand what has been said about this tool and, based on the growing literature that has emerged on the subject, demystify its effective impact on society. To analyse this impact, a systematic literature review with the support of the preferred reporting items for systematic reviews and meta-analysis protocol was used. The data, scientific documents, were collected using the main scientific databases – SCOPUS and Web of Science – and the results were presented based on a bibliometric and thematic exploration of content. The main findings indicate that people are increasingly using this chatbot in more diverse areas. Therefore, this study contributes at the practical level, aiming to enlighten people in general – both in professional and personal life – about this tool and its impacts. Also, it contributes at the theoretical level, which involves expanding understanding and elucidation of the impacts of ChatGPT in different areas of study. [ABSTRACT FROM AUTHOR]

Food fraud poses a significant challenge within the global food supply chain, with apprehensions regarding safety, authenticity, and efficiency. This study conducts a brief review of the literature by utilizing the Web of Science database, analyzing 2331 outcomes pertaining to the subject of food fraud. The analysis results demonstrated a noteworthy surge in scientific publications after 2013, which was propelled by events such as the horsemeat scandal and the formation of the European Food Safety Authority. Utilizing Multiple Correspondence Analysis (MCA), the study identified significant clusters pertaining to food transformation, safety, traceability, and distinct meat sources. In addition, trending topics shifted towards a holistic approach to food safety and the implementation of technologies like Blockchain (BC), Internet of Things (IoT), Artificial Intelligence (AI), and Big Data (BD). These technologies offer enhanced traceability, authentication, automation, and decision-making capabilities. The present research offers valuable perspectives on the evolving landscape of food fraud research and the potential of nascent technologies to tackle these issues. [ABSTRACT FROM AUTHOR]

Recent progress in the realm of artificial intelligence has shown effectiveness in various industries, particularly within the healthcare sector. However, there are limited insights on existing studies regarding ethical, social, privacy, and technological aspects of AI in the health sector, which is the gap our study aims to fill. This study aimed to synthesize empirical studies on the challenges and opportunities of using AI by conducting a systematic review. We reviewed 33 articles published between 2015 and 2022 in the PubMed, IEEE Xplore, and Science Direct databases. The results show that artificial intelligence has the promise of improving health care and faces obstacles when implemented. Most of the reviewed studies indicated that the use of AI provides several opportunities, including teamwork and decision-making, technological advancement, diagnosis and patient monitoring, drug development, and virtual health assistance. However, the findings show that the use of AI in the health sector hinders multifaceted challenges, including ethical and privacy-related issues, lack of awareness, unreliability of technology, and professional liability. The findings highlight that artificial intelligence has the potential to transform healthcare and that addressing these challenges is crucial to fully utilize its potential. [ABSTRACT FROM AUTHOR]

Copyright of Selcuk Dental Journal is the property of Selcuk Dental Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study aims to conduct research by making use of studies investigating the negative effects of artificial intelligence on the future careers and work motivation of tourism employees. Design/methodology/approach: In this research, a literature review, which is one of the qualitative research methods, was used. The study was completed by using a total of 13 articles and two book chapters investigating the negative aspects of artificial intelligence in the research data Science Direct and Web of Science databases as the main references. Findings: In the articles examined as a result of the research, it was predicted that the entry of artificial intelligence into the tourism sector poses a threat to the future careers of many tourism employees, and this will cause tourism employees to lose their focus and motivation at work. Another conclusion reached as a result of the research is that many tourism workers will be unemployed in the future due to artificial intelligence-supported information systems and robots. Originality/value: When the literature was reviewed, there was no research that directly examined the negative effects of artificial intelligence on tourism sector employees. Therefore, this research is unique and important in this respect. [ABSTRACT FROM AUTHOR]

Purpose: Developments regarding the use of artificial intelligence (AI) in transportation systems, one of the important stakeholders of tourism, are remarkable. However, no review thus far has provided a comprehensive overview of research on AI in transportation systems. Design/methodology/approach: To fill this gap, this study uses the VOSviewer software to present a bibliometric review of the current scientific literature in the field of AI-related tourism research. The theme of AI in transportation systems was explored in the Web of Science database. Findings: The original search yielded 642 documents, which were then filtered by parameters. For publications related to AI in transportation systems, the most cited documents, leading authors, productive countries, co-occurrence analysis of keywords and bibliographic matching of documents were examined. This report shows that there has been a recent increase in research on AI in transport systems. However, there is only one study on tourism. The country that contributed the most is China with 298 studies. The most used keyword in the documents was intelligent transportation system. Originality/value: The bibliometric analysis of the existing work provided a valuable and seminal reference for researchers and practitioners in AI-related in transportation system. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to focus on publications that jointly address robots in the tourism field and the technology acceptance model (TAM). Design/methodology/approach: This study adopts bibliometric analysis. Publications listed in the Web of Science database constitute the scope of this research. 51 publications were analyzed within the scope of the research. Findings: Between the years 2017 and 2023, an upward trend in the number and citations of publications was identified. It has been observed that article studies are more prevalent compared to other types of publications. When considering the indexes of the publications, a significant majority were found to be in Social Sciences Citation Index (SSCI) and Science Citation Index (SCI)-EXPANDED. The status of the keywords identified within the scope of the research in the abstracts of the publications has been presented. The keyword "robot" was found to be the most frequently occurring in the abstracts. The abstracts were also analyzed, and the publications were accordingly clustered into five distinct themes. Originality/value: This study offers a comprehensive evaluation of publications concerning the use of robots in the tourism sector, framed within the context of the TAM. Within the scope of the study, the findings were interpreted using bibliometric analysis. The publications have been categorized into themes. The results presented provide insights into the necessity for further publications in this field. [ABSTRACT FROM AUTHOR]

The marketing literature highlights the growing integration of artificial intelligence (AI) into marketing strategies. Several publications show that this field is attracting increasing interest from researchers. The purpose of this article is to provide an overview of academic publications related to AI and marketing strategies, while also examining the lack of bibliometric analysis in this area. In this study, 1100 articles, published in the Scopus and Web of Science databases, were selected and, according to a consistent search procedure, were examined. A performance analysis, based on bibliometric indicators, revealed the most impactful journals, the most indexed authors according to H-index and the most cited papers. The thematic factorial map highlighted the typology of AI tools used in the field of strategic marketing, in this case the marketing strategy. It also provides a discussion, potential research avenues and recommendations for future investigations. [ABSTRACT FROM AUTHOR]

Aim To assess machine-learning models, their methodological quality, compare their performance, and highlight their limitations. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations were applied. Electronic databases Science Direct, MEDLINE through (PubMed, Google Scholar), EBSCO, ERIC, and CINAHL were searched for the period of January 2016 to September 2023. Using a pre-designed data extraction sheet, the review data were extracted. Big data, risk assessment, colorectal cancer, and artificial intelligence were the main terms. Results Fifteen studies were included. A total of 3,057,329 colorectal cancer (CRC) health records, including those of adult patients older than 18, were used to generate the results. The curve's area under the curve ranged from 0.704 to 0.976. Logistic regression, random forests, and colon flag were often employed techniques. Overall, these trials provide a considerable and accurate CRC risk prediction. Conclusion An up-to-date summary of recent research on the use of big data in CRC prediction was given. Future research can be facilitated by the review's identification of gaps in the literature. Missing data, a lack of external validation, and the diversity of machine learning algorithms are the current obstacles. Despite having a sound mathematical definition, area under the curve application depends on the modelling context. [ABSTRACT FROM AUTHOR]

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The purpose of this study is to systematically analyze the impact of Industry 4.0 technologies on workforce employability and skills in the South Asian region. The study investigates the driving success factors, challenges, and needed skills by analyzing 48 peer-reviewed articles. The authors searched keywords on the Web of Science database for articles published between 2013 and 2022. The review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA 2020) and pareto principles. The analysis identifies nine critical success factors, such as artificial intelligence, digital skills, and big data analytics, that contribute to Industry 4.0's productivity and efficiency. It also identifies six types of challenges, such as training and development, financial constraints, and regulatory issues that must be addressed to grab maximum potential. In addition, the research categorizes five different skills, including the technical, digital, and social skills that are essential for the evolving labor market. The proposed "Industry 4.0 SEI Framework" provides stakeholders with a comprehensive view of the dynamics of Industry 4.0, thereby facilitating policy and industry strategies. [ABSTRACT FROM AUTHOR]

Background and Aims: This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis. Methods: On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta‐Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements. Results: After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms. Conclusion: The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision‐making process for healthcare providers. [ABSTRACT FROM AUTHOR]

Objective: To understand the current status of research in the field of metaverse, and to analyze the research progress and evolutionary trends in this field. Methods: Based on the bibliometric analysis, a total of 921 papers were obtained by searching the Web of science core database for the keyword "metaverse". CiteSpace was used to visualize and analyze the current status and trends of metaverse research in China. Results: Ireland is currently the country with the highest research impact. China is currently the country with the largest number of publications in this field, but the impact of the research is insufficient. The current research in the highly cited literature focuses on technical and history reviews of the metaverse as well as its development in the field of education. Artificial Intelligence and utaut2 are the underlying clusters of cited literature in this research area. Several research hotspots have been formed, such as virtual reality, augmented reality, mixed reality, digital twins and artificial intelligence. Conclusion: The current research on metaverse in various fields is basically in its infancy, but has a great potential for future development and will gradually penetrate into many different directions with many challenges. [ABSTRACT FROM AUTHOR]

The field of neurosurgery has consistently represented an area of innovation and integration of technology since its inception. As such, machine learning (ML) has found its way into applications within neurosurgery relatively rapidly. Through this bibliometric review and cluster analysis, we seek to identify trends and emerging applications of ML within neurosurgery. A bibliometric analysis was carried out in the Web of Science database on publications from January 2000 to March 2023. The full data set of the 200 most cited publications including title, author information, journal, citation count, keywords, and abstracts for each publication was evaluated in CiteSpace. CiteSpace was used to elucidate publication characteristics, trends, and topic clusters via collaborate network analysis using the Kamada-Kawai algorithm. The 25 most cited titles were included in our analysis. Harvard University and its affiliates represented the top institution, contributing nearly 25% of publications in the literature. WORLD NEUROSURGERY was the journal with the highest net citation count of 747 (29%). Collaborative network analysis generated 12 unique clusters, the largest of which was machine learning, followed by feature importance and deep brain stimulation. This review highlights the most impactful articles pertaining to ML in the field of neurosurgery. ML has been applied into several sub-specialties within neurosurgery to optimize patient care, with special attention to outcome predictors, patient selection, and surgical decision making. [ABSTRACT FROM AUTHOR]

Since its release at the end of 2022, the social response to ChatGPT, a large language model (LLM), has been huge, as it has revolutionized the way we communicate with computers. This review was performed to describe the technical background of LLMs and to provide a review of the current literature on LLMs in the field of oral and maxillofacial surgery (OMS). The PubMed, Scopus, and Web of Science databases were searched for LLMs and OMS. Adjacent surgical disciplines were included to cover the entire literature, and records from Google Scholar and medRxiv were added. Out of the 57 records identified, 37 were included; 31 (84%) were related to GPT-3.5, four (11%) to GPT-4, and two (5%) to both. Current research on LLMs is mainly limited to research and scientific writing, patient information/communication, and medical education. Classic OMS diseases are underrepresented. The current literature related to LLMs in OMS has a limited evidence level. There is a need to investigate the use of LLMs scientifically and systematically in the core areas of OMS. Although LLMs are likely to add value outside the operating room, the use of LLMs raises ethical and medical regulatory issues that must first be addressed. [ABSTRACT FROM AUTHOR]

The article describes the creation of tools for building intelligent systems in the field of scientometrics as the concept of the Internet of Things. The concept is presented as an information system where scientometric databases such as Scopus, WoS, as well as sites with databases of scientific projects and other databases act as sensors that generate data. Systems of data accumulation and processing are systems created using microservices technology. At the output, we get a control signal in the form of a proposal to correct the trajectory of the development of the scientific potential as an organization or a scientist. The system will also suggest how to strengthen the research team by selecting scientific partners from the global scientific community. The article describes the structural model of the concept of the Internet of Things in the form of a multi-level structure using microservice technologies. Also, the article suggests methods of constructing a scalar evaluation of the results of scientific research activities of scientists based on the analysis of citations of their publications. [ABSTRACT FROM AUTHOR]

Accurate assessment of HER2 expression in tumor tissue is crucial for determining HER2-targeted treatment options. Nevertheless, pathologists' assessments of HER2 status are less objective than automated, computer-based evaluations. Artificial Intelligence (AI) promises enhanced accuracy and reproducibility in HER2 interpretation. This study aimed to systematically evaluate current AI algorithms for HER2 immunohistochemical diagnosis, offering insights to guide the development of more adaptable algorithms in response to evolving HER2 assessment practices. A comprehensive data search of the PubMed, Embase, Cochrane, and Web of Science databases was conducted using a combination of subject terms and free text. A total of 4994 computational pathology articles published from inception to September 2023 identifying HER2 expression in breast cancer were retrieved. After applying predefined inclusion and exclusion criteria, seven studies were selected. These seven studies comprised 6867 HER2 identification tasks, with two studies employing the HER2-CONNECT algorithm, two using the CNN algorithm, one with the multi-class logistic regression algorithm, and two using the HER2 4B5 algorithm. AI's sensitivity and specificity for distinguishing HER2 0/1+ were 0.98 [0.92–0.99] and 0.92 [0.80–0.97] respectively. For distinguishing HER2 2+, the sensitivity and specificity were 0.78 [0.50–0.92] and 0.98 [0.93–0.99], respectively. For HER2 3+ distinction, AI exhibited a sensitivity of 0.99 [0.98–1.00] and specificity of 0.99 [0.97–1.00]. Furthermore, due to the lack of HER2-targeted therapies for HER2-negative patients in the past, pathologists may have neglected to distinguish between HER2 0 and 1+, leaving room for improvement in the performance of artificial intelligence (AI) in this differentiation. AI excels in automating the assessment of HER2 immunohistochemistry, showing promising results despite slight variations in performance across different HER2 status. While incorporating AI algorithms into the pathology workflow for HER2 assessment poses challenges in standardization, application patterns, and ethical considerations, ongoing advancements suggest its potential as a widely effective tool for pathologists in clinical practice in the near future. [ABSTRACT FROM AUTHOR]

Introduction: Soft tissue sarcomas (STS) are a rare group of malignancies. Diagnostic biopsies require experts and are invasive with long diagnostic intervals. Artificial intelligence (AI) based STS models may be accurate detection and categorization tools; however, their diagnostic performance is questionable. Method: The PubMed, Scopus, and Web of Science databases were searched for related studies published until January 10,2024. Studies that developed or used AI-based models to diagnose STS were included. Results: Eleven studies were included in this meta-analysis. The common effects model yielded an accuracy of 0.8923 [0.8831; 0.9016], and the random-effects model yielded an accuracy of 0.8524 [0.8132; 0.8916], The Tau2 was 0.0094 [0.0055; 0.0202], and the I² statistic was 93.2% [91.1%; 94.7%], suggesting a high level of heterogeneity among the studies. The most accurate model was the Decision Tree (DT) model used in the study by Alaoui et al. (2021), with an accuracy of 0.9900 [0.9675; 1.0125], The least accurate model was the MLP model used in the study by Yang et al. (2021), with an accuracy of 0.5720 [0.5107; 0.6333], Conclusion: AI-based models show promising results for the diagnosis of STS. However, future studies should address the value of AI in the real-world setting. [ABSTRACT FROM AUTHOR]

Stillbirth is a major global issue, with over 5 million cases each year. The multifactorial nature of stillbirth makes it difficult to predict. Artificial intelligence (AI) and machine learning (ML) have the potential to enhance clinical decision-making and enable precise assessments. This study reviewed the literature on predictive ML models for stillbirth highlighting input characteristics, performance metrics, and validation. The PubMed, Cochrane, and Web of Science databases were searched for studies using AI to develop predictive models for stillbirth. Findings were analyzed qualitatively using narrative synthesis and graphics. Risk of bias and the applicability of the studies were assessed using PROBAST. Model design and performance were discussed. Eight studies involving 14,840,654 women with gestational ages ranging from 20 weeks to full term were included in the qualitative analysis. Most studies used neural networks, random forests, and logistic regression algorithms. The number of predictive features varied from 14 to 53. Only 50% of studies validated the models. Cross-validation was commonly employed, and only 25% of studies performed external validation. All studies reported area under the curve as a performance metric (range 0.54–0.9), and five studies reported sensitivity (range, 60– 90%) and specificity (range, 64 − 93.3%). A stacked ensemble model that analyzed 53 features performed better than other models (AUC = 0.9; sensitivity and specificity > 85%). Available ML models can attain a considerable degree of accuracy for prediction of stillbirth; however, these models require further development before they can be applied in a clinical setting. [ABSTRACT FROM AUTHOR]

In today's world, it is vital to have strong cybersecurity measures in place. To combat the ever-evolving threats, adopting advanced models like cybersecurity mesh is necessary to enhance our protection. Cybersecurity mesh is an architecture scalable, flexible, composable, robust and resilient and allows the interoperability and coordination between intelligent systems to provide security services. Designing a cybersecurity mesh faces three major challenges: scalability, distributed or federated systems, and technology integration. For the design, it is necessary to apply security tools that support scalability because millions and millions of data are stored, processed, and analysed. Federated systems are needed to improve interoperability in a decentralized cybersecurity mesh. However, it can be tough to integrate different security tools and communication protocols. Cryptographic algorithms and AI models like federated learning, swarming intelligence and blockchain technologies are useful for security services. It is essential to study the integration of existing methods to determine the best technology for the job. We conduct a comprehensive analysis of intelligent systems, including federated learning, blockchain technology, and swarming intelligence, with a particular focus on how they have been and can be used to enhance cybersecurity. We examine the latest trends in these technologies, explore their connections, and weigh the pros and cons of each approach. To conduct this review, we utilized the Web of Science and Scopus databases and followed the PRISMA guidelines. [ABSTRACT FROM AUTHOR]

The wide availability of electronic medical data from multiple sources, such as clinical settings and wearable devices, evidences an important opportunity for the analysis of non-communicable diseases with high mortality rates globally and costly management expenditures, like cancer, diabetes, cardiovascular or chronic respiratory diseases. Advances in artificial intelligence (AI) have a great potential to use the information obtained from the constant monitoring of physiological parameters to create models that can serve as support in the warning of possible health complications, enabling medical professionals to provide anticipated care, thus benefiting national economies through reduction of medical expenses. The present study is a systematic review of different scientific databases taking two time periods, from 1956 to 2021 and from 2011 to 2021, to show the trends in the use of artificial intelligence algorithms and the performances obtained in the monitoring and detection of non-communicable diseases using electronic data. It is found that the analysis of cancer data predominates over that of other non-communicable diseases; also, the best performance of AI techniques was not achieved by artificial neural networks despite being the most reported computing systems in the time period considered. [ABSTRACT FROM AUTHOR]