Exploring the Use of AI in the Aviation Sector: A Comprehensive Bibliographic Evaluation

The main objective of this paper is to illustrate the use of artificial intelligence (AI) in the tourism and air transport industries to improve tourists’ experiences, as well as provide a definition of the AI concept closest to both sectors. In order to examine and demonstrate the body of literature on AI and its application to the travel and tourism industry. This study also presents the findings of a literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach in conjunction with a systematic literature review using the Web of Science (WoS) database. This approach enabled us to construct a novel AI concept in the context of tourism. This research found that AI technology offers new and creative opportunities for tourists due to this innovative tool that promotes and empowers travel and tourism organisations’ products and services. AI has helped to outline travel planning for tourists, made it easier to discover new experiences, and streamlined the booking process. The reality is that AI methods and applications are changing and improving passengers and tourists’ experiences in tourism cities and the air transport sector. Moreover, it is necessary to highlight that one of AI technology’s greatest strengths lies in the immediacy of response and advice that swiftly help tourists plan their trips, tours, detailed itineraries, and flight bookings at the same moment. This research is an antecedent attempt to define AI technology in the tourism and air transport context and to illustrate its virtues and shortcomings to improve tourists’ experiences in cities and the operational efficiency of organisations.

Artificial intelligence is revolutionizing hospital management by optimizing critical processes to improve operational efficiency. The automation of administrative tasks allows reducing errors and streamlining the flow of patients and work, which translates into lower costs and better use of hospital resources. The objective is to analyze research related to the optimization of hospital processes based on artificial intelligence. The research paradigm was qualitative-quantitative, the focus of this research was based on a bibliometric analysis, which was complemented with a documentary review in databases of high international and Latin American impact in the period from 2010 to 2024. The trend of the research was towards an increase, where research in the area of medicine and computer sciences predominated. A keyword co-occurrence and citation analysis were carried out to identify possible lines of research. It was identified that monitoring and predictive analytics technologies based on artificial intelligence enable proactive management of patients' health, preventing complications and optimizing resource allocation. These tools also facilitate the personalization of care, adjusting treatments according to the specific needs of each patient. The implementation of artificial intelligence in hospital processes is a crucial tool for improving operational efficiency and reducing costs through the automation of administrative tasks, resulting in a smoother and more effective operation

As artificial intelligence (AI) is becoming more integrated into the field of healthcare, medical students need to learn foundational AI literacy. Yet, traditional, descriptive teaching methods of AI topics are often ineffective in engaging the learners. This article introduces a new application of cinema to teaching AI concepts in medical education. With meticulously chosen movie clips from "Enthiran (Tamil)/Robot (Hindi)/Robo (Telugu)" movie, the students were introduced to the primary differences between artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI). This method triggered encouraging responses from students, with learners indicating greater conceptual clarity and heightened interest. Film as an emotive and visual medium not only makes difficult concepts easy to understand but also encourages curiosity, ethical consideration, and higher order thought. This pedagogic intervention demonstrates how narrative-based learning can make abstract AI systems more relatable and clinically relevant for future physicians. Beyond technical content, the method can offer opportunities to cultivate critical engagement with ethical and practical dimensions of AI in healthcare. Integrating film into AI instruction could bridge the gap between theoretical knowledge and clinical application, offering a compelling pathway to enrich medical education in a rapidly evolving digital age.NEW & NOTEWORTHY This article introduces a new learning strategy that employs film to instruct artificial intelligence (AI) principles in medical education. By introducing clips the from "Enthiran (Tamil)/Robot (Hindi)/Robo (Telugu)" movie to clarify artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI), the approach converted passive learning into an emotionally evocative and intellectually stimulating experience. Students experienced enhanced comprehension and increased interest in artificial intelligence. This narrative-driven, visually oriented process promises to incorporate technical and ethical AI literacy into medical curricula with enduring relevance and impact.

A few weeks ago, we published an editorial discussion on whether artificial intelligence applications should be authors of academic articles [1] . We were delighted to receive more than one interesting reply letter to this editorial in a short time [2, 3] . We hope that opinions on this

Despite exponential growth in artificial intelligence (AI) research for laboratory medicine and pathology, a significant gap exists between model development and clinical AI implementation. This article introduces a structured framework, the Clinical AI Readiness Evaluator (CARE), to bridge this gap and support the responsible adoption of AI in clinical laboratory settings. Building upon the Machine Learning Technology Readiness Levels framework, we developed CARE specifically for the clinical laboratory environment by incorporating health care-specific requirements, regulatory considerations, and workflow integration needs. This framework was iteratively refined through practical application across diverse AI use cases within laboratory medicine and pathology. The CARE framework provides a systematic approach to AI development and implementation through 8 component workstreams: clinical use case, data, data pipeline, code, clinical user experience, clinical technology infrastructure, clinical orchestration, and regulatory compliance. Unlike generic AI frameworks, CARE distinguishes itself by emphasizing both health care and laboratory workflow integration, regulatory requirements, ethical considerations, and comprehensive validation for clinical contexts. The framework accommodates both internally developed models and commercial AI solutions, providing clear guidance through technology readiness levels and structured review processes. The CARE framework addresses the unique challenges of implementing AI in laboratory medicine and pathology by providing a comprehensive roadmap from initial concepts through clinical deployment and maintenance. This article, the first in a series of 4, establishes the foundational AI lifecycle framework, while subsequent articles will explore data documentation, ethical AI considerations, and governance structures. By adopting this structured approach, laboratories can responsibly harness AI's potential to enhance diagnostic accuracy and operational efficiencies and, ultimately, improve patient care.

Artificial Intelligence (AI) in AI – induced neuro – inspired leadership refers to the integration of Artificial Intelligence technologies to enhance AI – induced neuro – inspired leadership practices, decision-making, and organizational performance. AI tools automate routine tasks, analyze data for insights, and improve strategic planning, ultimately allowing leaders to focus on more strategic and human-centric aspects of AI – induced neuro – inspired leadership. However, ethical considerations and potential biases in AI systems also need to be addressed. AI algorithms can analyze large datasets to provide leaders with valuable insights and predictions, enabling more informed and data-driven decisions. AI assists in identifying trends, forecasting outcomes, and optimizing resource allocation, leading to more effective strategic planning. AI-powered tools can automate routine tasks, streamline workflows, and optimize processes, freeing up leaders’ time for more strategic initiatives. Leaders need to be aware of potential biases in AI algorithms and develop ethical frameworks to ensure fairness and accountability in the use of AI. AI can be used to identify and develop future leaders, personalize AI – induced neuro – inspired leadership training, and ensure smooth succession planning. AI-powered analytics tools can provide insights into employee performance, customer behavior, and market trends. Chat bots and virtual assistants can handle routine inquiries, freeing up human staff for more complex tasks. AI-driven talent management systems can help organizations identify and develop future leaders. AI-powered tools for automation can streamline workflows and improve efficiency across various departments. Protecting sensitive data used in AI systems is crucial. Ensure that AI algorithms are free from bias and promote fairness in decision-making is essential. Some employees may be resistant to the integration of AI, requiring effective change management strategies. Leaders need to consider the ethical implications of using AI and develop appropriate guidelines. In conclusion, AI is transforming AI – induced neuro – inspired leadership by providing powerful tools for decision-making, strategic planning, and operational efficiency. However, it is crucial for leaders to understand the ethical implications and potential challenges associated with AI integration and to develop strategies for responsible and effective implementation.

Artificial Intelligence (AI) is increasingly discussed as a tool that can support speech and language therapy (SLT). However, clinical adoption of AI requires improved AI literacy among clinicians. AI is a rapidly evolving and often inconsistently defined field that can be difficult to navigate. Despite the definition provided by the EU AI Act, AI terminology can feel abstract for non-technical readers. To provide a foundational understanding of AI tailored for SLTs, by translating complex concepts into accessible language and organising them across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI can support SLT). This tutorial is informed by foundational AI literature, established AI taxonomies, relevant SLT literature and regulatory and ethical guidelines. Clinical analogies are used to explain technical concepts, with additional technical detail signposted where relevant. Existing and conceptual examples illustrate the relevance of AI across paediatric SLT practice. This tutorial provides: (i) a clinician-focussed interpretation of the EU AI Act definition; (ii) an organisation of key AI concepts into techniques, capabilities and clinical applications; (iii) a production-line model for mapping clinical needs to AI design choices and (iv) a practice-focussed discussion of ethical and regulatory considerations. AI is best understood as a set of techniques that enable specific capabilities, which in turn support clinical applications. This tutorial promotes the safe, ethical and accountable use of AI as a tool that can support rather than replace clinicians. What is already known on this subject Current Artificial Intelligence (AI) literature is typically designed for technical audiences, making it difficult for clinicians to interpret. This can hinder the effective and responsible integration of AI into clinical practice. What this paper adds to the existing knowledge This tutorial provides a clinician-focussed explanation of AI, structured across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI supports practice) in paediatric speech and language therapy. It also addresses key challenges, ethical considerations and regulatory requirements relevant to clinical contexts. What are the potential or actual clinical implications of this work? This tutorial lays the groundwork for informed engagement with emerging AI tools. It prepares clinicians to evaluate how different AI techniques and capabilities may support core clinical tasks (e.g., assessment, therapy planning and delivery).

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a provider-level performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

To explore the ethical considerations and challenges faced by nursing professionals in integrating artificial intelligence (AI) into patient care. AI's integration into nursing practice enhances clinical decision-making and operational efficiency but raises ethical concerns regarding privacy, accountability, informed consent, and the preservation of human-centered care. A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Thirteen studies were selected from databases including PubMed, Embase, IEEE Xplore, PsycINFO, and CINAHL. Thematic analysis identified key ethical themes related to AI use in nursing. The review highlighted critical ethical challenges, such as data privacy and security, accountability for AI-driven decisions, transparency in AI decision-making, and maintaining the human touch in care. The findings underscore the importance of stakeholder engagement, continuous education for nurses, and robust governance frameworks to guide ethical AI implementation in nursing. The results align with existing literature on AI's ethical complexities in healthcare. Addressing these challenges requires strengthening nursing competencies in AI, advocating for patient-centered AI design, and ensuring that AI integration upholds ethical standards. Although AI offers significant benefits for nursing practice, it also introduces ethical challenges that must be carefully managed. Enhancing nursing education, promoting stakeholder engagement, and developing comprehensive policies are essential for ethically integrating AI into nursing. AI can improve clinical decision-making and efficiency, but nurses must actively preserve humanistic care aspects through ongoing education and involvement in AI governance. Establish ethical frameworks and data protection policies tailored to AI in nursing. Support continuous professional development and allocate resources for the ethical integration of AI in healthcare.

In the digital age, the intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration of this multifaceted relationship. This study aims to quantify and characterize the current research trends and critically assess the discourse on ethical AI applications within HE. Employing a mixed-methods design, we integrated quantitative data from the Web of Science, Scopus, and the Lens databases with qualitative insights from selected studies to perform scientometric and content analyses, yielding a nuanced landscape of AI utilization in HE. Our results identified vital research areas through citation bursts, keyword co-occurrence, and thematic clusters. We provided a conceptual model for ethical AI integration in HE, encapsulating dichotomous perspectives on AI’s role in education. Three thematic clusters were identified: ethical frameworks and policy development, academic integrity and content creation, and student interaction with AI. The study concludes that, while AI offers substantial benefits for educational advancement, it also brings challenges that necessitate vigilant governance to uphold academic integrity and ethical standards. The implications extend to policymakers, educators, and AI developers, highlighting the need for ethical guidelines, AI literacy, and human-centered AI tools.

The implementation of artificial intelligence (AI) involves the utilization of various methodologies, including machine learning, deep learning, natural language processing, and robotics. These methodologies enable computers to perform tasks that traditionally require human intelligence, such as recognizing patterns in data, understanding natural language, and making decisions. AI's future potential is vast and spans across numerous industries and applications. In healthcare, AI can assist in disease diagnosis, personalized treatment plans, and drug discovery. In finance, AI algorithms can analyze market trends, detect fraud, and optimize investment portfolios. In transportation, AI powers autonomous vehicles, optimizing routes and reducing accidents. In entertainment, AI-driven recommendation systems personalize content for users. However, realizing this potential requires addressing several challenges. Ethical considerations are paramount, including ensuring fairness, transparency, accountability, and privacy in AI systems. Bias in algorithms must be mitigated to prevent discrimination, and AI decisions must be interpretable and explainable to users. Research efforts are underway to advance AI capabilities while addressing these challenges. This includes developing more robust and efficient algorithms, improving AI safety and security, and exploring applications of AI for social good, such as addressing climate change and promoting inclusivity. Collaboration between academia, industry, and government is crucial for driving AI research forward and ensuring its responsible integration into society. This collaboration facilitates knowledge sharing, resource allocation, and the development of standards and regulations to guide the ethical and equitable deployment of AI technologies. Ultimately, by harnessing the power of AI responsibly, society can benefit from its transformative potential while mitigating potential risks.

The general and specific factors influencing the formation and development of the air transport industry as a sphere of international entrepreneurship are considered. Differences in approaches to “international entrepreneurship” are identified, allowing it to highlight the main features, expand its content, and define the category of “international entrepreneurship in the air transport industry.” The types and forms of international entrepreneurship in the air transport industry are systematized. The main differences between the air transportation market and other transportation services markets are determined. The forms and types of international entrepreneurship in the air transport industry are detailed. It is proved that for the air transport industry, international entrepreneurship is an objective process caused by international economic relations between economic entities of the air transport industry, enterprises of related industries, organizations, and government agencies in the areas that ensure the implementation of all operations aimed at meeting the demand that exists at the level of the world economy for passenger and cargo air transportation (supply and maintenance of aircraft, ground handling of passengers and cargo, ground handling of passengers and cargo, ground handling of air transport). This allowed us to define international entrepreneurship in the air transport industry as a set of organizational and economic mechanisms that ensure interaction between different countries’ institutional units to create conditions and carry out international air transportation. The main goal of international business in the air transport industry is to ensure the efficient functioning and development of the aviation industry on a global scale, to increase the availability of air transportation for passengers, and to strengthen the positions of companies in the global market. As a result of the study of the evolution of international economic relations, the problematic aspects characteristic of the current stage of development are identified. The influence of global crisis phenomena on international entrepreneurship development, particularly in the air transport industry, is analyzed. Keywords: international entrepreneurship, international economic relations, air transport industry, conditions and factors of development, integration processes, globalization.

Abstract: This article provides an analysis of the current state and future prospects of Artificial Intelligence (AI) implementation in law enforcement. As advancements in technology continue to reshape various sectors, the integration of AI in policing has become a focal point, revolutionizing traditional methods and offering new opportunities. The article begins by outlining the contemporary landscape of AI applications in law enforcement, encompassing predictive policing, facial recognition, data analysis, and crime pattern identification. The discussion delves into the benefits and challenges associated with these technologies, addressing concerns related to privacy, bias, and ethical considerations. Furthermore, the article explores the evolution of AI in law enforcement, examining how machine learning algorithms enhance predictive capabilities, streamline investigative processes, and contribute to proactive crime prevention. It also highlights successful case studies and realworld implementations, showcasing the positive impact AI has had on solving complex criminal cases and optimizing resource allocation. In exploring development prospects, the article considers emerging trends such as explainable AI, human-AI collaboration, and continuous advancements in data analytics. The importance of responsible AI deployment is emphasized, emphasizing the need for transparent and ethical frameworks to guide law enforcement agencies. The article concludes by envisioning a future where AI technologies are seamlessly integrated into law enforcement practices, fostering improved crime detection, community safety, and overall operational efficiency. The insights presented aim to contribute to informed discussions surrounding the responsible and effective use of AI in the evolving landscape of law enforcement. Keywords: Artificial intelligence; Data analysis; Digital automation; Law enforcement; Facial recognition systems

Artificial intelligence is widely used in many fields, including education. Artificial intelligence is an important application field in educational guidance, management, evaluation, etc. Through the systematic review and analysis of artificial intelligence theory, it is applied to the study and life evaluation of college students. Therefore, in the past 5 years (2015-2022), this paper has published literature collection database of Science Network (WOS) and searched relevant literature published on CNKI. Found a total of 2336 papers, with the aid of CiteSpace clustering analysis function, keywords co-occurrence and time zone map system analysis. Can be seen from the analysis in the study, in most studies, artificial intelligence application in the education behavior behind the pedagogy less well, at the same time, discovered the formative assessment AI seems to be the evaluation mainly USES. Artificial intelligence (ai) in the evaluation of teaching is a main function of student to carry on the automatic grading. Using CiteSpace countries, institutions, and artificial intelligence research topic is analyzed, in order to get the research field of artificial intelligence and hot spots, the application of artificial intelligence in education evaluation, in turn, provide reference for subsequent research.

The intersection of artificial intelligence (AI) and neuroticism has gained increasing scholarly attention, influencing domains such as mental health, human–computer interaction, and AI-driven behavioural analysis. Despite growing research, there is limited synthesis of trends, influential contributors, and emerging themes in this field. This study aims to provide a comprehensive bibliometric overview of AI applications in neuroticism research. The study draws on personality psychology and bibliometric theory to map the intellectual landscape. Concepts such as trait neuroticism, AI-driven behavioural modelling, and thematic clustering underpin the analysis. Bibliometric approaches – including co-citation, bibliographic coupling, and keyword co-occurrence – provide the framework to examine research development and collaboration patterns. A systematic bibliometric analysis was conducted on 321 Scopus-indexed articles. Data were analysed using co-occurrence of keywords, co-citation analysis, thematic clustering, and bibliographic coupling. The study identified key authors, institutions, journals, citation patterns, and thematic clusters, covering research from 2015 to 2025. Eight major thematic clusters were identified, including AI-driven personality assessment, social media behaviour, trust in AI, and emotion recognition. The USA and China emerged as the most prolific contributors, with the University of Pennsylvania leading in institutional productivity. Computers in Human Behaviour and Frontiers in Psychology were the most influential journals. The findings highlight gaps in AI-driven psychological interventions, ethical considerations, and cross-cultural studies. Future research should focus on deep learning techniques for personality modelling, human–AI trust dynamics, and integrating AI into psychological assessments. This bibliometric mapping provides a roadmap to guide interdisciplinary research at the intersection of AI and personality psychology.