The Current Use of Artificial Intelligence in Anesthesiology.

Artificial intelligence (AI) applications in healthcare and considerations for nursing education

Readiness for Artificial Intelligence in Biobanking

This research aims to measure the influence of dependence on the use of artificial intelligence (AI) applications on the learning effectiveness of Islamic Education Management students. Using quantitative research methods, data was collected through questionnaires distributed to 150 students at several universities. Data analysis was carried out using a simple linear regression test to determine the correlation between the dependency variable on AI applications (X) and learning effectiveness (Y). The research results show that 78% of respondents use AI applications in their daily learning activities, with 65% of them feeling more efficient in accessing information. However, there are 40% of students who show decreased motivation for independent learning due to dependence on AI applications. The results of the regression test show that there is a significant positive correlation between the use of AI applications and learning effectiveness with a correlation coefficient of 0.52 and a significance of p < 0.05. These findings indicate that the use of AI plays an important role in increasing learning effectiveness, but also has the potential to reduce motivation for independent learning. It is hoped that the use of AI will be accompanied by learning strategies that support student independence and critical thinking.

Over the last decade, there has been great research interest in the application of artificial intelligence (AI) in various fields, such as medicine, finance, and law. Recently, there has been a research focus on the application of AI in education, where it has great potential. Therefore, a systematic review of the literature on AI in education is therefore necessary. This article considers its usage and applications in Latin American higher education institutions. After identifying the studies dedicated to educational innovations brought about by the application of AI techniques, this review examines AI applications in three educational processes: learning, teaching, and administration. Each study is analyzed for the AI techniques used, such as machine learning, deep learning, and natural language processing, the AI tools and algorithms that are applied, and the main education topic. The results reveal that the main AI applications in education are: predictive modelling, intelligent analytics, assistive technology, automatic content analysis, and image analytics. It is further demonstrated that AI applications help to address important education issues (e.g., detecting students at risk of dropping out) and thereby contribute to ensuring quality education. Finally, the article presents the lessons learned from the review concerning the application of AI technologies in higher education in the Latin American context.

The widespread use and adoption of Artificial Intelligence (AI) applications among university students has drastically transformed the educational landscape. Recognizing the importance of this transformation, this study aims to investigate the factors affecting the adoption and use of AI applications among Pakistani research scholars. This study used an extended version of the unified theory of acceptance and use of the technology model and innovative resistance theory. The data were collected from 235 research scholars through a questionnaire. Descriptive statistics and a multiple linear regression test were used to analyze the collected data. The study found that Pakistani research scholars used various AI applications for research purposes such as ChatGPT, Grammarly, ChatPDF, and SciSpace. This study found that personal innovativeness, performance expectancy, social influence, and trust significantly influence research scholars’ behavioral intention to use AI applications. In contrast, the impact of effort expectancy, facilitating conditions, and resistance to innovation on students’ behavioral intention to use AI tools was statistically insignificant. The findings offer actionable insights for educators, policymakers, and technology developers aiming to enhance AI adoption in higher education.

BackgroundCurrently, artificial intelligence (AI) has been widely used for the prediction, diagnosis, evaluation and rehabilitation of stroke. However, the quantitative and qualitative description of this field is still lacking.ObjectiveThis study aimed to summarize and elucidate the research status and changes in hotspots on the application of AI in stroke over the past 20 years through bibliometric analysis.Materials and MethodsPublications on the application of AI in stroke in the past two decades were retrieved from the Web of Science Core Collection. Microsoft Excel was used to analyze the annual publication volume. The cooperation network map among countries/regions was generated on an online platform (https://bibliometric.com/). CiteSpace was used to visualize the co-occurrence of institutions and analyze the timeline view of references and burst keywords. The network visualization map of keywords co-occurrence was generated by VOSviewer.ResultsA total of 4437 publications were included. The annual number of published documents shows an upwards trend. The USA published the most documents and has the top 3 most productive institutions. Journal of Neuroengineering and Rehabilitation and Stroke are the journals with the most publications and citations, respectively. The keywords co-occurrence network classified the keywords into four themes, that is "rehabilitation," "machine learning," "recovery" and "upper limb function." The top 3 keywords with the strongest burst strength were "arm," "upper limb" and "therapy." The most recent keywords that burst after 2020 and last until 2023 included "scores," "machine learning," "natural language processing" and "atrial fibrillation."ConclusionThe USA shows a leading position in this field. At present and in the next few years, research in this field may focus on the prediction/rapid diagnosis of potential stroke patients by using machine learning, deep learning and natural language processing.

Interest in learning is a high inclination towards something, supported by passion and desire, and becomes a motivation to carry out certain behaviors, including in the context of learning. The use of Artificial Intelligence (AI) applications to assist with lectures and completing course assignments is becoming an increasingly popular phenomenon among students. By using AI applications, students get fast and efficient data access to learning resources, resulting in more accurate results and saving the time needed to complete assignments. This research aims to find out whether there is an influence from the use of AI applications on the learning interest of Electrical Engineering students. This research is descriptive research with quantitative methods. The population of this study were all active electrical engineering students at Padang State University. while the samples were taken using the Solvin formula. The research process was carried out by distributing questionnaires via Google Forms. The results of this research show that there is a significant influence between the use of AI applications on electrical engineering students' interest in learning and the direction of the influence is positive.

This paper discusses the use of artificial intelligence (AI) applications for the creation and enrichment of descriptive metadata at meemoo, the Flemish Institute for Archives. We begin by explaining why we use AI tools for metadata creation and enrichment, and which AI applications we specifically employ. We then describe our evolution from small-scale pilots to large-scale projects, and how meemoo aims to transition from a project-based approach to a structural operation in using AI applications for metadata creation and enrichment. We provide details on our approach, results, lessons learned and future steps, and describe how the use of AI applications poses not only technical challenges but also raises a series of legal and ethical questions. This paper highlights our journey into AI as a service organisation supporting over 180 organisations in the cultural, media and government sectors in Flanders — not in isolation but in close collaboration with our partners, leading to shared solutions.

Artificial Intelligence (AI) is transforming healthcare at a rapid rate by enhancing disease prevention, diagnosis, treatment planning and management of health systems. AI technologies such as machine learning, deep learning, and natural language processing have been able to show an enormously huge scale capacity for improvement in oral health outcomes at the population level, particularly among disadvantaged groups. AI based applications in preventive dentistry can identify high risk individuals for dental caries, periodontal infections and oral cancer to allow targeted dental care. Deep learning algorithms can also be applied in imaging diagnostics and dental care can be personalised using patient’s history, habits and genetics. AI applications can also be utilised in epidemiologic surveillance for analysis of health-related information in forecasting disease patterns and drafting evidence based public health policies. AI can enhance dental education and training of new dental professionals through simulation-based education and intelligent tutoring systems. Administrative efficiency can also be achieved using AI based management software for patient scheduling, billing, and resource management which can reduce the workload of the clinician and enhance the functioning of health system. Inspite of the widespread applications of AI in dental public health, ethical issues related to data security, unequal access to technology and regulatory policies need to be addressed. Transparency in AI usage, accountability and equitable incorporation of AI in health systems are required. With a focus on diagnostic tools, epidemiological monitoring, individualised care, and health system management, this review article attempts to investigate the various uses of AI in public health dentistry. In the present review article, future prospects for AI in the public health dentistry field are explored, as well as its possible advantages and difficulties.

The traditional signature-based measures of cybersecurity faced growing challenges due to advanced cyber threats. Cyber AI, on the other hand, aided in automating dynamic and adaptive threat mitigation frameworks that can negate both known and unknown risks in real time. This paper explores the application of machine learning (ML), deep learning (DL), and natural language processing (NLP) in the context of AI-powered threat detection in current cybersecurity infrastructures. This paper starts off by identifying gaps around conventional detection tools that relied on static heuristics and rule-based methods, and didn‘t perform well against zero-day attacks, polymorphic malware, or advanced persistent threats (APTs) encounters. Also, integrating AI into these frameworks allows the use of predictive analytics and behavioural modelling to automate counteractive measures that identify, classify, and neutralise exploits. The examined methodologies also include malware classification using supervised and unsupervised learning algorithms, intrusion detection using neural networks, and analysing threat intelligence from phishing emails using NLP. The fast growth of cyber threats in their style, size, and smart tactics has made normal rulebased safety measures less useful. As a result, Artificial Intelligence (AI) is now seen as a game changer in finding dangers; it provides flexible, smart, and quick solutions that can spot and reduce both familiar and unfamiliar risks instantly. This paper reviews in detail AIdriven threat discovery, emphasising the use of machine learning (ML), deep learning (DL), and natural language processing(NLP) methods within current frameworks. The study begins by contextualising where conventional threat detection methods, rule-based systems and static heuristics fall short in combating zero-day exploits. malware and advanced persisten threats (APTs). Contrarily, AI-driven approaches use predictive analytics, behavioural modelling, and automated response mechanisms for anomaly recognition as well as classification of malicious activities to threats neutralisation prior to escalation. Major methodologies covered include: i) the supervised and unsupervised ML algorithms for malware classification; ii) neural networks for intrusion detection; and iii) NLP for threat intelligence analysis from sources like phishing emails or even dark web forums. It also examines recent developments in deep learning, including CNNs for image-based malware analysis and RNNs for identifying structured attack patterns in network traffic. It also addresses the aspect of how it considers generative adversarial networks in the process of simulating attacks on reinforcing defence systems. Also, this piece of work describes the improved outcome achieved from integrating AI with Security Information and Event Management systems, where threat correlation by machines and real-time response to incidents significantly lower detection and remediation time. Significant challenges that AI-based threat detection confronts in spite of its multiple advantages include adversarial attacks meant to mislead the ML models, limited training data leading to scarcity for creating strong systems, and the "black-box" nature of AI decisionmaking, coupled with lack of transparency and accountability. The moral consequences on potential biases in threat categorisation as well as privacy considerations of ubiquitous AI surveillance, are thoroughly examined.

Background: The integration of artificial intelligence (AI) into healthcare represents a transformative shift in medical procedures, offering substantial benefits across various domains. With advancements in AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP), healthcare systems are witnessing improvements in early detection, patient treatment, and overall administration. This article traces the evolution of AI, from foundational contributions by Alan Turing during World War II to contemporary applications like ChatGPT, and examines the impact of AI in enhancing diagnostic accuracy and treatment outcomes. Methods: This comprehensive review analyzes the existing literature on AI applications in healthcare, focusing on various AI methodologies and their integration into clinical settings. It evaluates the effectiveness of AI in processing large datasets, improving diagnostic precision, and facilitating data-driven decision-making. The study also explores the ethical, legal, and technical challenges associated with AI deployment in medical environments. Results: AI technologies have demonstrated significant improvements in healthcare, particularly in early disease detection, personalized treatment plans, and resource management. The use of AI in analyzing vast medical datasets has enhanced diagnostic accuracy, reduced costs, and optimized patient care. However, challenges related to ethical considerations, patient privacy, and system reliability remain critical barriers to full-scale AI adoption. Conclusion: Despite the challenges, AI is positioned as an indispensable tool in modern medicine, capable of enhancing preventive care, personalizing treatments, and improving healthcare delivery. This review proposes a framework for evaluating the benefits, challenges, and strategies of AI integration in healthcare. Further research is essential to maximize AI's potential while addressing ethical and practical concerns, ensuring safe and effective implementation in clinical settings.

This training aims to enhance the effectiveness of scientific writing for teachers in Baso, Agam Regency, West Sumatra, with the aid of Artificial Intelligence (AI) applications. In the context of expanding education, it is crucial for teachers to produce quality and relevant scientific writing. However, the scientific writing process often presents various challenges. Therefore, this training aims to enhance understanding of basic AI concepts and teach the effective use of relevant AI applications in scientific writing. The training includes presentations, discussions, practical demonstrations, and self-directed exercises to provide trainees with the necessary knowledge and skills. The training evaluation results indicate that participants improved their understanding of AI concepts, mastered the use of AI applications, and reported significant improvements in the efficiency and quality of their scientific writing. Additionally, the training successfully enhanced participants' digital literacy and made a positive contribution to education in the Baso region. It is expected that the training results will be practically applied in teachers' learning and research activities, and will have a sustainable impact on the development of education in the area. Keywords: Artificial Intelligence (AI), effectiveness, training, scientific writing, teachers

Despite the claim that artificial intelligence (AI) has the potential to increase efficiency in and for airlines, current literature is limited concerning models and frameworks to assess AI applications and their implications for airline efficiency. In response, we a) conceptualize and propose an AI-Airline-Efficiency-Model (AAEM) that allows for a more structured management approach for a systematic review and analysis of existing literature, and b) present a framework explicating the identified areas of AI application for airline efficiency based on a the AAEM model. In particular, using the four AI elements Machine Learning, Deep Learning, Reinforcement Learning and Natural Language Processing and their applications within six identified airline departments, we systematically review and analyze key attributes and characteristics of both AI and airline efficiencies to critically assess current research efforts. We found that AI applications are built around four overarching improvement areas predictive analytics, resource optimization, safety & autonomous processes and passenger experience, but lack a cross-department and inter-organizational focus and are often theoretical in nature. This study provides insight into most prevalent AI applications and the less popular applications applied in and for passenger transport, thereby presenting the dominating AI techniques that are covered by existing literature as well as highlighting a wide range of emerging AI techniques with the potential to become more influential for future studies. We discuss theoretical and managerial implications and offer avenues for future research.

Artificial Intelligence (AI) is playing an increasingly vital role in the field of financial statement analysis. AI methodologies, such as Deep Learning, Machine Learning, and Natural Language Processing, are being employed to enhance the analysis of financial data. This study explores the applications of AI in financial statement analysis, including Predictive Analytics, Anomaly Detection, Trend Analysis, Financial Report Automation, Risk Evaluation and Control, Comparative Analysis and Benchmarking, Portfolio Optimization, Risk Assessment and Management, Fraud Detection and Forensic Analysis, and Regulatory Reporting and Compliance. The chapter includes case studies, industrial insights, and practical applications of AI in financial statement analysis, highlighting the tangible benefits and opportunities for businesses in the finance sector. Additionally, the chapter addresses the challenges and potential future applications of AI in this domain.

Artificial intelligence (AI) has revolutionized numerous industries, including healthcare. With its ability to process large volumes of data and make predictions, AI has shown significant potential in improving diagnostics, treatment planning, and patient care. This paper presents a comprehensive survey of recent research and applications of AI in the medical field. The survey covers various AI techniques such as machine learning, deep learning, and natural language processing, and their applications in areas such as disease diagnosis, image analysis, drug discovery, and personalized medicine. Furthermore, this paper discusses challenges and ethical considerations associated with the adoption of AI in healthcare, highlighting the need for careful integration to ensure patient safety and privacy.