Topics In Artificial Intelligence Research Articles

A conceptual model of artificial intelligence effects on circular economy actions

AbstractThe circular economy (CE) presents a contemporary approach to integrating economic activity and environmental well‐being sustainably, opposing the linear open‐ended system. However, implementing this paradigm poses challenges, needing a fundamental shift in companies' strategies and business models. Emerging technology can support companies in transition toward CE and in particular artificial intelligence (AI) can play a crucial role in facilitating this transition by supporting companies not only in specific CE activities but also in strategically redefining their entire business models. Recognizing the increasing importance of both AI and CE in management literature, our paper seeks to integrate these two distinct streams of literature. Starting from existing studies, it tries to provide a framework able to understand the role of AI technology into enhancing CE. Through a systematic literature review using the Preferred Reporting Items for Systematic Review and Meta‐Analysis protocol, using keywords research on three databases, we identified 63 articles that concurrently address both CE and AI topics. Subsequent co‐occurrence and content analyses revealed how AI is utilized to bolster CE efforts leveraging the ReSOLVE framework (from the acronymous of Regenerate, Share, Optimise, Loop, Virtualise, and Exchange)—an operational tool for CE. The last step consists into the development of a conceptual framework outlining four stages of AI's engagement with the CE: namely System optimization, System redesign, Business Model redesign, and Ecosystem innovation. From our study emerges that while AI is already recognized for enhancing specific activities within CE, its potential as a strategic planning tool for business model redesign and ecosystem innovation remains largely unexplored.

Artificial Intelligence: Its Role and Potential in Education

Artificial intelligence (AI), which has attracted great attention in recent years, has been widely used in the field of education as in many other fields. AI in education is used to improve student learning, support teachers and provide a more personalized educational experience. AI plays an important role with adaptive learning systems in improving students' learning processes. These systems assess students' individual needs and provide them with appropriate learning materials. AI also monitors students' performance, identifies their weaknesses, and provides additional support in these areas. Thus, students are enabled to learn more effectively and to reveal their full potential. By supporting teachers, AI facilitates classroom management and helps teachers use their time more efficiently. Automated assessment systems allow teachers to quickly assess assignments and exams, while improving the process of providing feedback. In addition, AI also helps teachers understand students' interests and learning styles, so that more personalized instruction can be offered. Another important use of AI in education is student counseling. AI-based counseling systems can guide students in matters such as career choices, university applications, and academic planning. These systems can provide students with viable career options, support the application process, and help them identify their future goals. As a result, the use of AI in education has great potential to improve student learning processes, provide support to teachers and provide a more personalized educational experience. In this study; The subject of AI was examined in a general framework under the title of education and the role of AI in education was discussed. It is thought that AI will contribute to the field by revealing the teacher and how it can be used in the field of education.

Reasoning about Causality in Games (Abstract Reprint)

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. We then consider three key questions: i) How can the (causal) dependencies in games – either between variables, or between strategies – be modelled in a uniform, principled manner? ii) How may causal queries be computed in causal games, and what assumptions does this require? iii) How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library.

Метатренды и мифологемы тематики искусственного интеллекта западного научного дискурса

Introduction. For fifty years, the topic of artificial intelligence has remained leading within a wide range of scientific fields. Theoretical analysis. Preliminary analysis of the Scopus database results from a rating of publication activity, in which social sciences occupy insider positions along with computer sciences and some other areas. From the 60s of the past century to the present, there has been an exponential growth of scientific publications containing the label “artificial intelligence” in titles, abstracts and keywords. Empirical analysis. The article attempts, based on elementary numerical indi cators and qualitative content analytics, to identify trends in the subject of artificial intelligence in Western social sciences, the evolution of key research areas, as well as the main mythologies of scientific discourse. Conclusion. The research is preceded by the hypothesis: social sciences, owing to the specification of discourse and the subject spectrum, contain “naturalized” mythologems associated with other discursive practices and refractive scientific and technical interpretations of the subject of study.

Trends and Current Topics in Artificial Intelligence in Nursing Research: A Bibliometric Analysis and Science Mapping

Objective: As AI's role in nursing grows, it is vital to understand its impact and challenges. Using bibliometric analysis, this study aimed to identify and examine the prevailing trends and current topics in artificial intelligence research within nursing. Materials and Methods: This was a retrospective bibliometric study. Study data were collected from WoSCC on August 08, 2023. Analyses were made through science mapping, Microsoft Excel, and VOSviewer. Results: The study included 316 publications dated 1984-2023. There was a rapid increase in publications and citations from 2018-2023. Related publications were made by 1148 authors. The journal "CIN-Computers, Informatics, Nursing" emerged as the most frequently published and cited journal. Fifty-three countries contributed to the publications, of which 45.2% were produced in the USA. The current topics were patient safety, depression, ChatGPT, and Chatbot in recent years. Conclusion: This bibliometric study shows a synergy between the general policies of countries on artificial Intelligence in recent years and the increasing number of publications in the last four years. However, this study also reveals that research on artificial intelligence in nursing is a nascent field. Managers and research nurses should lead the use of AI applications in nursing services management and nursing training and should encourage research on the topic. Key Words: Artificial Intelligence, Nursing, Bibliometric Analysis, Research Trends, VOSviewer

Impact of artificial intelligence on aeronautics: An industry-wide review

Curiously, there are few contributions in the scientific literature on the subject of artificial intelligence (AI) and its impact on aeronautics. However, many communications and reports have been published by aeronautic companies about their applications of AI technologies. This article makes an industry-wide review of AI in aeronautics using a three-step sequential approach: (i) a review of AI and its concepts to define and develop a conceptual map; (ii) a selection of 100 use cases from aeronautics companies that use AI technologies (e.g., Airbus, Boeing, Air France, Safran, EasyJet, Dassault Aviation, Altair); and (iii) an analysis of the use cases using the topics defined in the conceptual map. The main results describe a rising interest in the integration of AI technologies by entities in the aeronautic sector. Moreover, the results from the use cases show that the most recurrent technologies are big data analytics, autonomous intelligent systems, predictive analytics, machine learning, and robotics. Another finding is related to the several benefits that motivate companies to integrate AI technologies into their industrial and operational processes. The most frequent benefits include customer satisfaction, saving time, safety and security, cost reduction, better decision making, solving complex problems, and ensuring optimisation and efficiency. It also appears that the performance of companies is positively impacted by using these AI technologies. Such impacts span all operational departments including marketing, where these technologies help satisfy customer needs; the industrial and operational area, which is provided with quality products; and where productivity and economic performance are optimised for more efficiency.

Evolving landscape of artificial intelligence (AI) and assessment in education: A bibliometric analysis

The rapid evolution of digital technologies and computer sciences is ushering society into a technologically driven future where machines continually advance to meet human needs and enhance their own intelligence. Among these groundbreaking innovations, Artificial Intelligence (AI) is a cornerstone technology with far-reaching implications. This study undertakes a bibliometric review to investigate contemporary AI and assessment topics in education, aiming to delineate its evolving scope. The Web of Science Databases provided the articles for analysis, spanning from 1994 to September 2023. The study seeks to address research questions about prominent publication years, authors, countries, universities, journals, citation topics, and highly cited articles. The study’s findings illuminate the dynamic nature of AI in educational assessment research, with AI firmly establishing itself as a vital component of education. The study underscores global collaboration, anticipates emerging technologies, and highlights pedagogical implications. Prominent trends emphasize machine learning, Chat GPT, and their application in higher education and medical education, affirming AI's transformative potential. Nevertheless, it is essential to acknowledge the limitations of this study, including data currency and the evolving nature of AI in education. Nonetheless, AI applications are poised to remain a prominent concern in educational technology for the foreseeable future, promising innovative solutions and insights.

Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions’ students in Jordan

IntroductionThe integration of Artificial Intelligence (AI) in medical education and practice is a significant development. This study examined the Knowledge, Attitudes, and Practices (KAP) of health professions’ students in Jordan concerning AI, providing insights into their preparedness and perceptions.MethodsAn online questionnaire was distributed to 483 Jordanian health professions’ students via social media. Demographic data, AI-related KAP, and barriers were collected. Quantile regression models analyzed associations between variables and KAP scores.ResultsModerate AI knowledge was observed among participants, with specific understanding of data requirements and barriers. Attitudes varied, combining skepticism about AI replacing human teachers with recognition of its value. While AI tools were used for specific tasks, broader integration in medical education and practice was limited. Barriers included lack of knowledge, access, time constraints, and curriculum gaps.ConclusionsThis study highlights the need to enhance medical education with AI topics and address barriers. Students need to be better prepared for AI integration, in order to enable medical education to harness AI’s potential for improved patient care and training.

Cultivation of human centered artificial intelligence: culturally adaptive thinking in education (CATE) for AI.

Artificial Intelligence (AI) has become ubiquitous in human society, and yet vast segments of the global population have no, little, or counterproductive information about AI. It is necessary to teach AI topics on a mass scale. While there is a rush to implement academic initiatives, scant attention has been paid to the unique challenges of teaching AI curricula to a global and culturally diverse audience with varying expectations of privacy, technological autonomy, risk preference, and knowledge sharing. Our study fills this void by focusing on AI elements in a new framework titled Culturally Adaptive Thinking in Education for AI (CATE-AI) to enable teaching AI concepts to culturally diverse learners. Failure to contextualize and sensitize AI education to culture and other categorical human-thought clusters, can lead to several undesirable effects including confusion, AI-phobia, cultural biases to AI, increased resistance toward AI technologies and AI education. We discuss and integrate human behavior theories, AI applications research, educational frameworks, and human centered AI principles to articulate CATE-AI. In the first part of this paper, we present the development a significantly enhanced version of CATE. In the second part, we explore textual data from AI related news articles to generate insights that lay the foundation for CATE-AI, and support our findings. The CATE-AI framework can help learners study artificial intelligence topics more effectively by serving as a basis for adapting and contextualizing AI to their sociocultural needs.

Comparative analysis of the topics of scientific publications in the field of artificial intelligence in international and Russian journals

Aim. To perform a comparative analysis of the topics and volumes of publication activity in the field of artificial intelligence (AI) of Russian authors in journals indexed in such databases as the Russian Science Citation Index (RSCI) and WoS/Scopus, in relation to the fields of AI technologies, scientific directions and organisations of the place of work.Tasks. To create a list of publications by authors with Russian affiliation in journals indexed in WoS/Scopus, including those categorised as Q1 and Q2 quartiles, as well as in the proceedings of A* level conferences, the topics of which are related to the AI sphere; to create a list of publications by Russian researchers devoted to the AI topics in journals indexed in RSCI, including those categorised as Q1; to carry out a comparative analysis of the topics and structure of publications by Russian authors in journals indexed in WoS/Scopus and RSCI in relation to the fields of AI technologies, groups of scientific specialties, and organisations of the authors’ place of work.Methods. The methodology of the study is based on expert analysis of publications in scientific journals indexed in WoS/Scopus, including Q1 and Q2 quartiles, in proceedings of A* level conferences, scientific journals indexed in RSCI for 2017–2021, the subject matter of which relates to the field of AI, based on selections from open databases of publications using libraries and packages for data processing and analysis. The criterion for attributing an article to the AI subject was the presence of word combinations from the fields of AI technologies and subtechnologies in the search fields “publication title” and (or) “Keywords”, “Abstract”.Results. Comparison of highly rated WoS/Scopus, Q1, Q2, A* and RINC K1 publications shows that among WoS/Scopus, Q1, Q2, A* publications “Machine learning: knowledge representation, ontology, interpretation, explanation” (32 %) and “Computer vision” (30 %) dominate. Among RINC Q1 publications, “Responsible Artificial Intelligence (ethics and philosophy, legal regulations, security)” (34 %), “Machine learning: knowledge representation, ontology, interpretation, explanation” (28 %) and “Intelligent analysis of data and processes, making sense of patterns” (13 %) dominate.Conclusions. The analysis of publication activity of Russian authors in relation to AI topics on the basis of comparison of scientific publications from WoS/Scopus and RINC databases can serve as an information basis for selection of AI competence centres and formation of lists of best practices in the field of AI.

Artificial Intelligence for Education and Research: Pilot Study on Perception of Academic Staff

Artificial Intelligence (AI) tools have been used across various sectors of the global economy. The use of AI has been associated with both benefits and drawbacks, which is why the goal of this research was to identify the attitudes of academic staff of higher education institutions (HEIs) towards using AI for both academic and research purposes. To attain the goal, there was designed a questionnaire, which was distributed to members of academic staff of different biological genders and ages from 10 European countries. Irrespective of biological gender, age, or country, responses were similar. First, academic staff emphasized the importance of having AI-related regulations at HEIs. Second, academic staff were positive about using AI for information searchers and preparation of teaching materials. Third, academic staff were concerned about AI-related plagiarism issues, which is why they were reluctant to approve the AI use for research and thesis writing. Fourth, slightly more than 40% of the respondents indicated the use of AI. This points to the lack of AI skills among academic staff, which was further supported by a set of basic purposes for which AI was claimed to have been used. One implication of this research relates to the organization of the study process. Managers of HEIs should introduce institutionalized training in AI for academic and research purposes for academic staff to promote digital equality. Another implication of the study relates to the areas of AI training for academic staff. It should cover the topics of AI for the design of teaching materials, formative and summative assessment, and plagiarism check.

Artificial intelligence in food biotechnology: trends and perspectives

Artificial Intelligence (AI) is the creation of intelligent systems able to execute specific tasks which would normally need human intelligence to function. Today, AI is widely used to assist research and development in industrial biotechnology. Many biotech firms rely on AI tools. The food industry is among the industrial sectors where AI has only recently been explored. AI enhances food biotechnology by supporting food enzymes engineering, microbial metabolic engineering, food safety and, in general, food microbiology. The use of AI tools in the food industry ranges from food processing – including all aspects of the production of food ingredients – to food quality and safety. This paper highlights the emerging importance of AI in some specific sectors of food biotechnology: development of new process-aid enzymes, precision fermentation, food safety and toxicity, food microbiology, and the study of the interplay between food and gut microbiome. These represent only a few examples of the myriad of applications that AI is seeing and will see in the food biotech sector. This Perspective paper intends to stimulate the discussion around the intricate relationship between AI and such research topics.

AI Course Design Planning Framework: Developing Domain-Specific AI Education Courses

The use of artificial intelligence (AI) is becoming increasingly important in various domains, making education about AI a necessity. The interdisciplinary nature of AI and the relevance of AI in various fields require that university instructors and course developers integrate AI topics into the classroom and create so-called domain-specific AI courses. In this paper, we introduce the “AI Course Design Planning Framework” as a course planning framework to structure the development of domain-specific AI courses at the university level. The tool evolves non-specific course planning frameworks to address the context of domain-specific AI education. Following a design-based research approach, we evaluated a first prototype of the tool with instructors in the field of AI education who are developing domain-specific courses in this area. The results of our evaluation indicate that the tool allows instructors to create domain-specific AI courses in an efficient and comprehensible way. In general, instructors rated the tool as useful and user-friendly and made recommendations to improve its usability. Future research will focus on testing the application of the tool for domain-specific AI course developments in different domain contexts and examine the influence of using the tool on AI course quality and learning outcomes.

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges.

There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.

ARDA‐UNIT recurrent dense self‐attention block with adaptive feature fusion for unpaired (unsupervised) image‐to‐image translation

AbstractOne of the most challenging topics in artificial intelligence is image‐to‐image translation, the purpose of which is generating images close to those in the target domain while preserving the important features of the images in the source domain. In this direction, various types of generative adversarial networks have been developed. ARDA‐UNIT, presented in this paper, seeks to meet the main challenges of these networks, that is, producing a high‐quality image in a reasonable amount of time, and transferring content between two images with different structures. The proposed recurrent dense self‐attention block, applied in ARDA‐UNIT's generator latent space, simultaneously increases its generating capability and decreases the training parameters. ARDA‐UNIT has a feature extraction module which feeds both the generator and the discriminator. This module uses a new adaptive feature fusion method which combines multi‐scale features in such a way that the characteristics of each scale are preserved. The module also uses a pre‐trained CNN that reduces the training parameters. Moreover, a feature similarity loss is introduced that guides the model to change the structure of the source domain in accordance with that in the target domain. Experiments performed on different datasets using FID, KID and IS evaluation criteria have shown that the model reduces computational loads, transfers structures well, and achieves better qualities.

Curriculum frameworks and educational programs in artificial intelligence for medical students, residents, and practicing physicians: a scoping review protocol

Objective:The aim of this scoping review is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of artificial intelligence (AI) for medical students, residents, and practicing physicians.Introduction:To advance the implementation of AI in clinical practice, physicians need to have a better understanding of AI and how to use it within clinical practice. Consequently, medical education must introduce AI topics and concepts into the curriculum. Curriculum frameworks are educational road maps to teaching and learning. Therefore, any existing AI curriculum frameworks must be reviewed and, if none exist, such a framework must be developed.Inclusion criteria:This review will include articles that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of articles and study designs will be included, except conference abstracts and protocols.Methods:This review will follow the JBI methodology for scoping reviews. Keywords will first be identified from relevant articles. Another search will then be conducted using the identified keywords and index terms. The following databases will be searched: MEDLINE (Ovid), Embase (Ovid), Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL (EBSCOhost), and Scopus. Gray literature will also be searched. Articles will be limited to the English and French languages, commencing from the year 2000. The reference lists of all included articles will be screened for additional articles. Data will then be extracted from included articles and the results will be presented in a table.

Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews.

The use of artificial intelligence (AI) in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize health care, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current literature suggests the integration of the subject of AI in medicine as part of the medical curriculum to prepare medical students for the opportunities and challenges related to the use of the technology within the clinical context. We aimed to explore the relevant knowledge and understanding of the subject of AI in medicine and specify curricula teaching content within medical education. For this research, we conducted 12 guideline-based expert interviews. Experts were defined as individuals who have been engaged in full-time academic research, development, or teaching in the field of AI in medicine for at least 5 years. As part of the data analysis, we recorded, transcribed, and analyzed the interviews using qualitative content analysis. We used the software QCAmap and inductive category formation to analyze the data. The qualitative content analysis led to the formation of three main categories ("Knowledge," "Interpretation," and "Application") with a total of 9 associated subcategories. The experts interviewed cited knowledge and an understanding of the fundamentals of AI, statistics, ethics, and privacy and regulation as necessary basic knowledge that should be part of medical education. The analysis also showed that medical students need to be able to interpret as well as critically reflect on the results provided by AI, taking into account the associated risks and data basis. To enable the application of AI in medicine, medical education should promote the acquisition of practical skills, including the need for basic technological skills, as well as the development of confidence in the technology and one's related competencies. The analyzed expert interviews' results suggest that medical curricula should include the topic of AI in medicine to develop the knowledge, understanding, and confidence needed to use AI in the clinical context. The results further imply an imminent need for standardization of the definition of AI as the foundation to identify, define, and teach respective content on AI within medical curricula.

Artificial Intelligence for Upper Aerodigestive Tract Endoscopy and Laryngoscopy: A Guide for Physicians and State-of-the-Art Review.

The endoscopic and laryngoscopic examination is paramount for laryngeal, oropharyngeal, nasopharyngeal, nasal, and oral cavity benign lesions and cancer evaluation. Nevertheless, upper aerodigestive tract (UADT) endoscopy is intrinsically operator-dependent and lacks objective quality standards. At present, there has been an increased interest in artificial intelligence (AI) applications in this area to support physicians during the examination, thus enhancing diagnostic performances. The relative novelty of this research field poses a challenge both for the reviewers and readers as clinicians often lack a specific technical background. Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and Google Scholar. A structured review of the current literature (up to September 2022) was performed. Search terms related to topics of AI, machine learning (ML), and deep learning (DL) in UADT endoscopy and laryngoscopy were identified and queried by 3 independent reviewers. Citations of selected studies were also evaluated to ensure comprehensiveness. Forty-one studies were included in the review. AI and computer vision techniques were used to achieve 3 fundamental tasks in this field: classification, detection, and segmentation. All papers were summarized and reviewed. This article comprehensively reviews the latest developments in the application of ML and DL in UADT endoscopy and laryngoscopy, as well as their future clinical implications. The technical basis of AI is also explained, providing guidance for nonexpert readers to allow critical appraisal of the evaluation metrics and the most relevant quality requirements.

Reasoning about causality in games

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. We then consider three key questions:i)How can the (causal) dependencies in games – either between variables, or between strategies – be modelled in a uniform, principled manner?ii)How may causal queries be computed in causal games, and what assumptions does this require?iii)How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library.

The Impact of Quantum Computing on Cryptography

Abstract: The purpose of this paper's abstract is to explain how quantum computing works in terms of current cryptography and to provide the reader a rudimentary understanding of post-quantum algorithms. Community key encoding methods affected, symmetric structures affected, the influence on hash purposes, upright quantum cryptography, distinctions amongst quantum and standard computing, obstacles in quantum computation, and quantum procedures (Shor's and Grover's). The PostQuantum Cryptography section specifically discusses various mathematically based quantum crucial circulation techniques, lattice-built cryptography, multivariate-built cryptography, hash-based signs, and code-based encoding. One of the modern technologies in today's society is quantum computation. The advance of quantum computing applications is the focus of numerous communities and research institutions worldwide. Another developing field at the moment that is becoming stable is artificial intelligence. The major goal of this work is to determine the effects of the development of quantum computing research on applications involving artificial intelligence. Hence, computational methods are utilised in the study's methodology. So that this study's findings about the expanding impact of quantum computing research for a particular application of artificial intelligence can be drawn. The impact and potential of quantum computing on the subject of artificial intelligence is also discussed in this study, along with how quantum computing affects that discipline.