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Background: The utilization of ChatGPT in writing scientific papers has sparked both pros and cons in Indonesia. Some studies reveal its great potential, while others highlight the negative impacts resulting from the use of ChatGPT. Objective: This research aims to analyze the area, impact, and trends in the use of ChatGPT in writing scientific papers in Indonesia through a systematic review. Methodology: Researchers use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to conduct the analysis. The sample consists of 19 selected studies collected from the Google Scholar and Scopus databases. Data analysis uses quantitative and qualitative descriptive methods. Result: The research results show that the areas in which ChatGPT is used in writing scientific papers include topic selection, reference search, data analysis, scientific grammar, and translation. The use of ChatGPT in writing scientific papers faces some serious challenges, especially those related to ethics and academic integrity, such as increasing rates of plagiarism and declining values of honesty and responsibility. Moreover, dependence on artificial intelligence technology has the potential to reduce the development of human intellectual abilities, such as critical thinking, analysis, interpretation, and logic. Until recently, the research trend related to the use of ChatGPT for writing scientific papers is increasing, with the quite low density of research topics; thus, there are opportunities for further research to be carried out. Conclusion: The utilization of ChatGPT in academic writing in Indonesia has both positive and negative aspects. Regulation and morality can be crucial keys to realizing a quality academic environment. Unique Contribution: This research contributes to understanding the opportunities and challenges of utilizing ChatGPT in writing scientific papers, as well as providing information regarding areas that have the potential for further research. Key Recommendation: An in-depth understanding of the appropriate regulations for the use of ChatGPT in writing scientific papers is needed to minimize risks while still maximizing its positive potential.

The rapid development of artificial intelligence (AI) has significantly enhanced productivity, particularly in repetitive tasks. In the scientific domain, literature review stands out as a key area where AI-based tools can be effectively applied. This study presents a methodology for developing a search strategy for systematic reviews using AI tools. The Semantic Scholar database served as the foundation for the search process. The methodology was tested by searching for scientific papers related to batteries and hydrogen vehicles with the aim of enabling an evaluation for their potential applications. An extensive list of vehicles and their operational environments based on international standards and literature reviews was defined and used as the main input for the exemplary search. The AI-supported search yielded approximately 60,000 results, which were subjected to an initial relevance assessment. For the relevant papers, a neighbourhood analysis based on citation and reference networks was conducted. The final selection of papers, covering the period from 2013 to 2023, included 713 papers assessed after the initial review. An extensive discussion of the results is provided, including their categorisation based on search terms, publication years, and cluster analysis of powertrains, as well as operational environments of the vehicles involved. This case study illustrates the effectiveness of the proposed methodology and serves as a starting point for future research. The results demonstrate the potential of AI-based tools to enhance productivity when searching for scientific papers.

Introduction. Artificial intelligence technologies are increasingly finding way into various spheres of life. Academic research is no exception. In view of this, there is a need to study the independence (originality) of the thesis research, performed using a neural network, as the main criterion for its quality. Materials and Methods. The study, first of all, is based on the analysis and assessment of the opinions of various experts in the field of development of artificial intelligence systems, science and education. The study of different ways of conducting research in modern conditions was carried out using descriptive and comparative methods of scientific knowledge. Analysis. The study suggests an assessment of the fact that a work written with artificial intelligence technologies is recognized as an independently performed study. The opinions of various experts in the field of development of artificial intelligence systems, science and education are given. Various methods of conducting research in modern conditions are analyzed. Results. The authors believe that it is impossible to write an original study with a neural network, especially in the legal sphere, without active participation of a person. Since the originality of the study means conducting it for the first time using new methods, tools and obtaining new results and conclusions. A neural network (chat-GPT system) is not capable of creating new information (inventing something new) because it is based on texts that have been written by people before, and therefore it will not be able to conduct its own research. The final conclusion is made about the possibility of using an artificial intelligence system as a tool (means) to solve only standard problems (for example, checking errors, translating foreign literature, collecting generalized scientific material, designing work, etc.) in the process of writing a thesis.

Objective In recent years, advancements in information technology, such as wireless EMG, high-resolution cameras, programs like OpenSim, innovations in textile sensors, and the emergence of artificial intelligence and smart mobile devices, have provided biomechanists with new tools and approaches. This study aims to investigate emerging trends in sports biomechanics, summarizing and providing practical insights from research conducted between 2015 and 2023. Methods A systematic search of research articles on new biomechanics approaches published between 2015 and 2023 was conducted. Specialized databases were queried, and 47 articles meeting the inclusion criteria were selected for analysis. Results Analysis of the selected studies revealed that the integration of information technology, artificial intelligence, smartphones, software, and wearable medical sensors in sports biomechanics has shown promising results in enhancing performance and reducing injury risks. Conclusion The findings of this study suggest that advancements in sports biomechanics technologies are pushing the boundaries of current research. Continued exploration and application of these technologies will likely shape the future of sports science and performance.

Regulation of health technologies must be rigorous, instilling trust among both healthcare providers and patients. This is especially important for the control and supervision of the growing use of artificial intelligence in healthcare. In this commentary on the accompanying piece by Van Laere and colleagues, we set out the scope for applying artificial intelligence in the healthcare sector and outline five key challenges that regulators face in dealing with these modernday technologies. Addressing these challenges will not be easy. While artificial intelligence applications in healthcare have already made rapid progress and benefitted patients, these applications clearly hold even more potential for future developments. Yet it is vital that the regulatory environment keep up with this fast-evolving space of healthcare in order to anticipate and, to the extent possible, prevent the risks that may arise.

AIM: To analyze research hotspots and trends of artificial intelligence in diabetic retinopathy(DR)based on bibliometrics and high-impact papers.METHODS: Papers on artificial intelligence in DR research published in the Web of Science Core Collection(WoSCC)from January 1, 2012, to December 31, 2022 were retrieved. The data was analyzed by CiteSpace software to examine annual publication number, countries, institutions, source journal, research categories, keywords, and to perform an in-depth analysis of high-impact papers.RESULTS: A total of 1 009 papers on artificial intelligence in DR from 79 countries were included in the study, with 272 papers published in 2022. Notably, China and India contributed 287 and 234 papers, respectively. The United Kingdom exhibited a centrality score of 0.31, while the United States boasted an impressive H-index of 48. Three prominent institutions in the United Kingdom(University of London, Moorfields Eye Hospital, and University College London)and one institution in Egypt(Egyptian Knowledge Bank)all achieved a notable H-index of 14. The primary academic disciplines associated with this research field encompassed ophthalmology, computer science, and artificial intelligence. Burst keywords in the years 2021～2022 included transfer learning, vessel segmentation, and convolutional neural networks.CONCLUSION: China emerged as the leading contributor in terms of publication number in this field, while the United States stood out as a key player. Notably, Egyptian Knowledge Bank and University of London assumed leading roles among research institutions. Additionally, IEEE Access was identified as the most active journal within this domain. The research focus in the field of artificial intelligence in DR has transitioned from AI applications in disease detection and grading to a more concentrated exploration of AI-assisted diagnostic systems. Transfer learning, vessel segmentation, and convolutional neural networks hold substantial promise for widespread applications in this field.

The prospects for the use of artificial intelligence (AI) are one of the most discussed topics in medicine today. The very possibility of having an omniscient virtual assistant at hand soon seems incredibly tempting, so it seems quite normally to see numerous reports on the application of each newly emerging advanced neural network technology in various fields of medicine and biotechnology. Of course, the emergence of ChatGPT caused the greatest public outcry in recent times, because the new natural language processing algorithm underlying it has allowed human to bring communication between man and machine to a whole new level. Of course, despite the myriad benefits of using AI, the use of ChatGPT and other AI tools in medicine raises many ethical and legal questions. However, it is worth remembering the history of the emergence of any other breakthrough technology to accept the existing controversy as an integral part of progress. The desire of a person to make his work easier and shift part of the work onto a computer always makes him take a step forward in the development of technologies, which, in the end, do not allow a person to work less, but make him work in a new way.

The present study is an evaluation of published papers on machine learning as employed in mastitis research. The aim of this study was the quantitative evaluation of the scientific content and the bibliometric details of these papers. In total, 69 papers were found to combine machine learning in mastitis research and were considered in detail. There was a progressive yearly increase in published papers, which originated from 23 countries (mostly from China or the United States of America). Most original articles (n = 59) referred to work involving cattle, relevant to mastitis in individual animals. Most articles described work related to the development and diagnosis of the infection. Fewer articles described work on the antibiotic resistance of pathogens isolated from cases of mastitis and on the treatment of the infection. In most studies (98.5% of published papers), supervised machine learning models were employed. Most frequently, decision trees and support vector machines were employed in the studies described. ‘Machine learning’ and ‘mastitis’ were the most frequently used keywords. The papers were published in 39 journals, with most frequent publications in Computers and Electronics in Agriculture and Journal of Dairy Science. The median number of cited references in the papers was 39 (interquartile range: 31). There were 435 co-authors in the papers (mean: 6.2 per paper, median: 5, min.–max.: 1–93) and 356 individual authors. The median number of citations received by the papers was 4 (min.–max.: 0–70). Most papers (72.5%) were published in open-access mode. This study summarized the characteristics of papers on mastitis and artificial intelligence. Future studies could explore using these methodologies at farm level, and extending them to other animal species, while unsupervised learning techniques might also prove to be useful.

Scientific manuscripts are the basis for the transmission of scientific data among physicians in all fields of medicine. To teach young oncologists the skills needed to author a paper, we decided to emulate how experienced clinicians perform this task. The first step is to create a spreadsheet with all the clinical data gathered and submit it to a statistical analysis using a statistical software package. The most important results are presented in the graphs and tables. The results should be explained in a logical and understandable manner. Writing the “Materials and Methods” section follows, with all the technical information that any other researcher may need to reproduce elsewhere the study in question. A critical-thinking stage, in which a review of the pertinent literature is conducted with the use of a reference management software, should provide all the knowledge and questions to write the “Introduction” and “Discussion” sections. The “Abstract” and “Title” are the final sections to be created. Following these steps, the author can correct the first draft of the manuscript for submission to a specific journal. Choosing the right journal and answering the reviewers' comments are also important steps in this process. Even if a young oncologist does not embark on an academic career, learning how to write a scientific manuscript is believed to be the best way to teach them how to read such manuscripts during their lifelong continuous self-education.

Abstract This paper presents the case of an adjunct university professor to illustrate the dilemma of using artificial intelligence (AI) technology to grade student papers. The hypothetical case discusses the benefits of using a commercial AI service to grade student papers—including discretion, convenience, pedagogical merits of consistent feedback for students, and advances made in the field that yield high-quality work—all of which are achieved quickly. Arguments against using AI to grade student papers involve cost, privacy, legality, and ethics. The paper discusses career implications for faculty members in both situations and concludes with implications for researchers within the discourse on academic integrity.

We study a three-species cyclic model whose organisms are vulnerable to contamination with an infectious disease which propagates person-to-person. We consider that individuals of one species perform a self-preservation strategy by reducing the mobility rate to minimise infection risk whenever an epidemic outbreak reaches the neighbourhood. Running stochastic simulations, we quantify the changes in spatial patterns induced by unevenness in the cyclic game introduced by the mobility restriction strategy of organisms of one out of the species. Our findings show that variations in disease virulence impact the benefits of dispersal limitation reaction, with the relative reduction of the organisms’ infection risk accentuating in surges of less contagious or deadlier diseases. The effectiveness of the mobility restriction tactic depends on the deceleration level and the fraction of infected neighbours which is considered too dangerous, thus triggering the defensive strategy. If each organism promptly reacts to the arrival of the first viral vectors in its surroundings with strict mobility reduction, contamination risk decreases significantly. Our conclusions may help biologists understand the impact of defensive strategies in ecosystems during an epidemic.

Artificial intelligence or AI is a hot topic. There are currently 100+ million users of ChatGPT (GPT = generative pre-trained transformer), which was designed and implemented by OpenAI. This is a significant portion of the entire world population. Numerous accolades have been given to the initiative. However, some suggest that AI could be used for nefarious purposes, it may eliminate jobs, provide erroneous information, and it might be used for cheating at work or school. Such events may have already occurred during the few months since the inception of recent AI chatbots. Now might be the point to address this issue from the perspective of what helpfulness can be incurred by AI in scientific paper writing. This discussion stems from the recent querying and probing ChatGPT-4.0 for this purpose.

Artificial Intelligence, and Machine Learning especially, are becoming increasingly foundational to our collective future. Recent developments around generative models such as ChatGPT, and DALL-E represent just the tip of the iceberg in new gadgets that will change the way we live our lives. Convolutional Neural Networks (CNNs) and Transformer models are at the heart of advancements in the autonomous vehicles and health care industries as well. Yet these models, as impressive as they are, still make plenty of mistakes without justifying or explaining what aspects of the input or internal state, was responsible for the error. Often, the goal of automation is to increase throughput, processing as many tasks as possible in a short a period of time. For some use cases the cost of mistakes might be acceptable as long as production is increased above some set margin. However, in health care, autonomous vehicles, and financial applications, the cost of a mistake might have catastrophic consequences. For this reason, industries where single mistakes can be costly are less enthusiastic about early AI adoption. The field of eXplainable AI (XAI) has attracted significant attention in recent years with the goal of producing algorithms that shed light into the decision-making process of neural networks. In this paper we show how robust vision pipelines can be built using XAI algorithms with the goal of producing automated watchdogs that actively monitor the decision-making process of neural networks for signs of mistakes or ambiguous data. We call these robust vision pipelines, squinting pipelines.

Background: The influence of AI and Big Data on the healthcare sector and health delivery systems provides a hopeful path to achieving the Sustainable Development Goals (SDGs) by way of improved decision-making and operational efficiencies. Methodology: A review of existing literature was carried out to evaluate the current landscape of AI applications in healthcare and its potential implications for SDG achievement. Results: Big data is at the center of health transformation, which, combined with artificial intelligence and Internet of Things, enables real-time processing and analysis of huge datasets. Big data enhances customization in marketing, streamlines operations in manufacturing and redefines healthcare by means of precision medicine. Big data also helps to drive changes in society through better public policy choices and steps taken to ensure sustainable development. It is through its analytic power that big data can identify obscure patterns and insights which can lead to pioneering solutions and strategic growth. Conclusion: Applying big data and AI strategically plays a crucial role in advancing the sustainable development agenda. However, we must exercise responsibility when utilizing the power of data. It is important to enforce strict measures of protecting data so as to avoid its misuse and also empower underdeveloped regions with relevant tools and capabilities for efficient data management. This way proactive moves will make sure that all people benefit from the information revolution, and it helps to drive global progress on SDGs.

Phishing is one of the major web social engineering attacks. This has led to demand for a better way to predict and stop them in a commercial environment. This paper seeks to understand the research done in the field and analyse the next steps forward. This is done by focusing on what goes into the selection of proper features, from manual selection to the use of Genetic Algorithms such as ADABoost and MultiBoost. Then a look into the classifiers in use, Neural Networks and Ensemble algorithms which were prominent alongside some novel approaches. This information is then processed into a framework for cloud-based and client-based phishing website detection, alongside suggestions for possible future research and experiments that could help progress the field.

Abstract To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.

The world is undergoing a thoughtful revolution with the arrival of the intelligent information era. The central realms accompanying smart living such as transportation, entertainment, healthcare and smart cities are projected to improve service quality assuring a high-end user experience. Future mobile networks are projected to foster the future of ubiquitously connected data-intensive intelligent society powered with complete automation by seamless integrating of all sorts of wireless networks spread over the ground, underwater, air and space. This special issue aims to bring together foremost researchers in academia and engineering from various backgrounds to disseminate to the technical community an outline of emerging technologies, advanced architectures, challenges, open issues and future directions of modern networks in artificial intelligence, internet of things, and blockchain-based applications.

The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns.

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Abstract Blockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

Abstract Searching relevant papers is a fundamental task for the elaboration of secondary studies. This task is known to be tedious and time‐consuming when it is made manually, especially with the presence of several academic repositories. Recently, Semantic Scholar has emerged as a new artificial intelligence‐based search engine enabling a set of valuable features. The present study investigates the role of Semantic Scholar in retrieving relevant papers for performing secondary studies in software engineering. For this sake, an examination is performed to check the ability of Semantic Scholar to locate included papers in recent and well‐established secondary studies. Afterwards, a hybrid and automatic search strategy is introduced making use of Semantic Scholar as a sole search engine and it incorporates: automatic search, snowballing, and use of Computer Science Ontology (CSO) and Software Engineering Body of Knowledge (SWEBOK) for refining queries. The proposed strategy is validated by replicating the search of high‐quality secondary studies in the software engineering field. To guarantee objectivity, a systematic search is conducted of recent secondary studies published in the field since 2015. For the coverage test, Semantic Scholar is examined to locate primary papers of selected secondary studies and identify missing venues. The proposed search strategy is used to check the ability to retrieve primary papers of each secondary study. The systematic search yielded 20 high‐quality secondary studies with 1337 distinct primary papers. The coverage test revealed that Semantic Scholar covers 98.88% of the papers. The proposed search strategy enabled the full replication of 13 studies and more than 90% for the 7 remaining studies.

This paper originates from empirical studies of translation process, from the epistemological perspective, we can relate the translation process to how subjective spirit can attach to the external world. To unfold the translation process ontologically and epistemologically, we integrate the logical abduction inference with semantic theory "influence the best explanation". After a large amount of TAPs data collections and theoretical discussions in translation process empirical studies (including our research), we conclude that a translator dynamically employ both of the subjective translation units and objective translation units during the translation process by combing subjectivity and objectivity physically, mentally and philosophically; As the translating conduction went on --- the proceeding of deverbalization translation strategies and mental lexical access strategies is on, the translators flexibly and frequently appeal to abduction hypothesis and abduction reasoning. We end this paper with a reflection on the experimental design of the translation process empirical study: the Survivor-ship bias points out the judge's bias to high-proficiency participants; and the limitation of inductive reasoning and analogical reasoning lead to the relevant rigor -skepticism. We call for a closer interdisciplinary and a diversity research on philosophy, logic and empirical studies on translation process.

In this transformative study, machine learning (ML) and t‐distributed stochastic neighbor embedding (t‐SNE) are employed to interpret intricate patterns in colorimetric images of cold atmospheric plasma (CAP)‐treated water. The focus is on CAP's therapeutic potential, particularly its ability to generate reactive oxygen and nitrogen species (RONS) that play a crucial role in antimicrobial activity. RGB, HSV, LAB, YCrCb, and grayscale color spaces are extracted from the colorimetric expression of oxidative stress induced by RONS, and these features are used for unsupervised ML, employing density‐based spatial clustering of applications with noise (DBSCAN). The DBSCAN model's performance is evaluated using homogeneity, completeness, and adjusted rand index with a predictive data distribution graph. The best results are achieved with 3,3′,5,5′‐tetramethylbenzidine–potassium iodide colorimetric assay solution immediately after plasma treatment, with values of 0.894, 0.996, and 0.826. t‐SNE is further conducted for the best‐case scenario to evaluate the clustering efficacy and find the best combination of features to better present the results. Correspondingly, t‐SNE enhances clustering efficacy and adeptly handles challenging points. The approach pioneers dynamic and comprehensive solutions, showcasing ML's precision and t‐SNE's transformative visualization. Through this innovative fusion, complex relationships are unraveled, marking a paradigm shift in biomedical analytical methodologies.

Medical imaging is currently using artificial intelligence-based technologies to aid evaluate diagnostic information images, particularly in enforcing kidney stones. Artificial intelligence technology continues to develop, many studies show that deep learning is more widely used compared to traditional machine learning, so an Artificial intelligence system is needed to assist the accuracy of health diagnoses, thus helping in the field of radiology health. The aim of the research is to use artificial intelligence with deep learning models to help detect abnormalities in the kidneys. This research method is a literature review of Scopus data related to deep learning in medical imaging in detecting kidney stones. The results of using Artificial Intelligence in medical imaging can be used in diagnosing diseases including detecting Covid-19, musculoskeletal, calcium scores on Cardiac CT, liver tumors, urinary tract lesions, examination of the abdomen and kidney stones. Utilization of Artificial Intelligence in detecting kidney stones can be done with various classification models including XResNet-50, ExDark19, CystoNet, CNN, ANN. Using the right model and having a high accuracy value can help radiologists to accurately detect kidney stones.

Abstract This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution. Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient’s protocol, tracking the patient’s dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow.

In this study, we investigated the human capacity to generate randomness in decision-making processes using the rock-paper-scissors (RPS) game. The randomness of the time series was evaluated using the time-series data of RPS moves made by 500 subjects who played 50 consecutive RPS games. The indices used for evaluation were the Lempel–Ziv complexity and a determinism index obtained from a recurrence plot, and these indicators represent the complexity and determinism of the time series, respectively. The acquired human RPS time-series data were compared to a pseudorandom RPS sequence generated by the Mersenne Twister and the RPS time series generated by the RPS game’s strategy learned using the human RPS time series acquired via genetic programming. The results exhibited clear differences in randomness among the pseudorandom number series, the human-generated series, and the AI-generated series.

Leveraging artificial intelligence (AI), this study revolutionizes examination management and education in universities by developing an intelligent system encompassing comprehensive management, pre-examination activities, scheduling, and preparation. The system also features a quality management component for educational outcomes. An enhanced genetic algorithm introduces an adaptation function to optimize intelligent grouping, facilitating effective exam paper distribution. Applied at Y University, our innovative approach significantly refines exam paper difficulty (ranging between 0.5016 and 0.5581) and differentiation (0.3845 to 0.4596), showcasing the intelligent algorithm’s effectiveness in exam management and contributing valuable insights to educational research.

The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health; and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO2, mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process.

(Series Information) European Papers - A Journal on Law and Integration, 2024 9(3), 936-955 | Article | (Table of Contents) I. Introduction. – II. The state of use of AI tax enforcement algorithms in the EU. – II.1. The use of AI fiscal governance tools in context. – II.2. The typology of AI fiscal governance tools. – III. Should AI fiscal governance tools be regarded as high-risk systems? – III.1. The regulatory structure of the EU Artificial Intelligence Act. – III.2. The specific treatment of AI systems used by tax administrations. – IV. Conclusion. | (Abstract) Tax administrations are among State organs who leverage AI-technology the most. AI systems used by tax administrations have already led to scandals and seminal jurisprudence such as SyRI, eKasa or SS SIA. The most striking example is the Dutch toeslagenaffaire where using an AI model, the tax administration discriminated and profiled taxpayers based on their ethnicity, causing irreparable harms. Growing awareness over the proliferation of AI triggered the Commission to submit the proposal for the European Union Artificial Intelligence Regulation (EU AI Act) as a regulatory response to control these externalities. Yet, confusion remains around the treatment of AI systems leveraged by tax administrations in the AI Act. Without a category of their own, it is unclear whether AI systems used by tax administrations qualify as high-risk systems or not. This uncertainty in the current version of the proposal raises the following question: “based on a teleological interpretation of the draft proposal for the EU AI Act, should AI systems used by tax administrations be regarded as high-risk systems?” This question is addressed in two parts. Section 1 presents the state of use of AI systems by tax administrations in the entire EU and the typology of functions performed by these systems. Section 2 examines whether, under the current text of the proposal for the EU AI Act and the different positions of EU institutions, these systems should be regarded as high-risk systems.

(Series Information) European Papers - A Journal on Law and Integration, 2024 9(3), 978-997 | Article | (Table of Contents) The right to avoid self-incrimination forms part of the fundamental rights of the defence accompanying the public enforcement of European Union (EU) competition law. Thanks to this right, undertakings cannot be forced to produce guilt-admitting answers to the European Commission or national competition authorities applying arts 101 and 102 of the Treaty on the Functioning of the European Union (TFEU). Despite its general recognition, open questions relating to its scope and importance throughout enforcement procedures constrain its practical use. Although those questions are problematic as a matter of EU law in general, this Article submits that they also have a direct and significant impact on the ability for EU and Member States’ competition authorities to introduce artificial intelligence-backed enforcement tools. Against such background, the Article prospectively analyses how the right to avoid self-incrimination could constrain the design and use of tailored automated competition enforcement tools. The first part of the Article revisits the scope of the right to avoid self-incrimination as apparent from the Court of Justice of the European Union’s case law. It identifies and distinguishes three open questions which underlie the right’s application in EU competition law enforcement. The second part argues that those questions directly condition the ways in which artificial intelligence-backed public enforcement tools can be implemented at different stages of the investigation and decision-making. Anticipating litigation on those questions, the Article therefore calls for the right to avoid self-incrimination to be given more explicit attention when designing or introducing automated enforcement tools. | (Abstract) I. Introduction. – II. The right to avoid self-incrimination in EU competition law. – II.1. The right to avoid self-incrimination in the Charter of Fundamental Rights: Article 6 ECHR as a starting point. – II.2. A specific right to avoid self-incrimination in EU competition law enforcement? – III. Protection against self-incrimination and automated competition law enforcement. – III.1. Automated competition law enforcement as an emerging reality. – III.2. Three automated enforcement scenarios calling for increased attention to self-incrimination avoidance. – III.3. Future-proofing competition law enforcement requires future-proofing the right to avoid self-incrimination. – IV. Conclusion.

(Series Information) European Papers - A Journal on Law and Integration, 2024 9(3), 1018-1047 | Article | (Table of Contents) I. Introduction – II. AI in the EU Large-Scale Information Systems: The Case of ETIAS and VIS – II.1. ETIAS and VIS within Interoperability – II.2. ETIAS and VIS automated processing – II.3. How will the ETIAS and VIS automated processing work in practice? – III. The ETIAS and VIS Automated Processing and the Legal Constraints of Decisions Based Solely on Automated Means – III.1. The general rule in Article 22 GDPR and Article 24 EUDPR – III.2. Condition I: The result of ETIAS and VIS automated processing: A decision that significantly affects data subjects? – III.3. Condition II: Safeguards for data subjects - IV. Conclusions | (Abstract) In 2019, the EU laid down the groundwork for interoperability in the Area of Freedom, Security and Justice, envisaging the use of algorithmic tools that can qualify as AI systems under the AI Act. AI tools used by EU migration databases are subject to the safeguards and the protective measures for individuals provided for under the AI Act, such as art. 86 thereof. However, given the fundamental rights impact of AI technologies processing large amounts of personal data, it is worth focusing on data protection law as one of the main strongholds against violations caused by AI in EU border and migration systems. In this Article we apply data protection provisions on purely automated decisions and the Court of Justice’s case law to the AI-enabled processing envisaged under two information systems, ETIAS and VIS. This processing was conceived as a supporting tool for competent authorities. This Article argues that, despite aiming to avoid solely automated decisions, the ETIAS and VIS processing might inadvertently lead to automation “taking over” the decision-making process. By contrast, a substantive reading of art. 22(1) GDPR (and art. 24(1) EUDPR) should not only prohibit decisions taken without any form of human involvement, but also decisions based on meaningless human involvement. As a result, the ETIAS and VIS processing may progressively reduce the extent to which human caseworkers review and question the AI-generated recommendations. By analysing the implications of the AI-enabled processing envisaged in the current EU border regulation, the Article seeks to draw useful lessons for further adoption of trustworthy AI in the border and security ecosystem.

(Series Information) European Papers - A Journal on Law and Integration, 2024 9(3), 930-934 | Article | (Abstract) Technological disruption is drastically changing the way rules are made and applied. This special section, consisting of five articles, is dedicated to exploring the impact of automated decision-making (ADM), and in particular artificial intelligence (AI) systems, on the enforcement of EU law. To this end, rather than deriving legal requirements from general doctrines and principles of EU law (top-down), these articles explore how the promises and threats of AI systems arise and are addressed in different policy areas of EU law enforcement (bottom-up), namely in the fields of tax law, the law enforcement Directive, competition, migration, and asylum.

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.

This paper aims to expose and analyze the potential foundational role of Argumentation for Human-Centric AI, and to present the main challenges for this foundational role to be realized in a way that will fit well with the wider requirements and challenges of Human-Centric AI. The central idea set forward is that by endowing machines with the ability to argue with forms of machine argumentation that are cognitively compatible with those of human argumentation, we will be able to support a naturally effective, enhancing and ethical human-machine cooperation and “social” integration.

The proposed special issue seeks to explore the developments, implications, opportunities and challenges of artificial intelligence (AI) in the field of strategic communication.

This paper explores the impact of automation and artificial intelligence (AI) on the field of accounting. As AI and automation technologies advance rapidly, they are transforming traditional accounting practices, shifting the role of accountants from routine, repetitive tasks to more strategic, value-added services. The integration of AI in accounting enhances processes like financial reporting, fraud detection, and cost analysis by automating labor-intensive tasks and providing real-time, data-driven insights. The paper discusses the transition from historical financial accounting to a more consultative role for accountants, supported by AI-driven analytics and predictions. It also highlights the need for accountants to adapt by developing new skills, particularly in data analysis, AI, and technology. Furthermore, the study underscores the critical need for responsible AI usage, emphasizing the importance of transparency, data security, and compliance with regulatory standards. The paper concludes by examining the implications of AI for accounting education, requesting curriculum updates to equip future professionals with the necessary skills to thrive in the evolving landscape of the accounting profession.

Purpose – This paper aims to examine the complex balance between enthusiasm and skepticism regarding artificial intelligence (AI) integration in educational practices. It advocates for a cautious, evidence-based approach while addressing both opportunities and challenges, aligning with the United Nations Sustainable Development Goal 4 (SDG4) for Quality Education. Design/methodology/approach – Through critical analysis of current discourse surrounding AI in education, this paper synthesizes existing literature on both supportive and skeptical perspectives. The methodology involves systematic examination of past educational technology trends, current AI developments and their implications for teaching and learning. The paper develops its research agenda through careful consideration of existing empirical studies, theoretical frameworks and identifying gaps in current understanding. Findings – The analysis reveals that while AI offers promising potential for enhancing learning outcomes and educational accessibility, its integration presents significant challenges that require careful consideration. The paper identifies critical tensions between technological innovation and pedagogical values, highlighting areas where enthusiasm for AI adoption must be tempered with empirical evidence and critical evaluation. Current evidence suggests that successful AI integration requires balanced consideration of both opportunities and limitations, with particular attention to maintaining human-centered educational practices. Originality/value – This viewpoint provides a comprehensive framework for understanding the dialectic between AI’s educational potential and its limitations. By synthesizing both supportive and critical perspectives, it offers a nuanced approach to AI integration that acknowledges both opportunities and challenges. The article’s value lies in its systematic identification of key research priorities and its emphasis on evidence-based implementation strategies that serve educational goals while mitigating potential risks.

The need for sophisticated traceability systems has become essential in increasingly complex and globalized supply chains. The convergence of Blockchain (BC), Internet of Things (IoT), and Artificial Intelligence (AI) technologies offers promising solutions to enhance traceability systems across various sectors, particularly supply chain management (SCM). This paper presents a bibliometric and systematic literature review (SLR) to examine trends, research patterns, and methodologies in integrating BC IoT and AI into traceability systems. Bibliometric analysis of 530 documents from SCOPUS (2014–2024) identified key trends, while the SLR, conducted across multiple databases following PRISMA guidelines, refined the dataset to 43 peer-reviewed studies based on inclusion criteria. Recent research output has notably increased, focusing on agricultural supply chains and SCM, with India and China leading in publications. The analysis shows a predominance of experimental and hybrid methodologies, using Ethereum and Hyperledger Fabric as key platforms. Key trends include AI-driven analytics, real-time IoT data collection, and the need for secure, tamper-proof data by BC. However, interoperability, scalability, and standardization challenges hinder adoption. The paper proposes a four-layer framework for integrating BC, IoT, and AI to improve transparency, security, and efficiency and highlights the need for more empirical studies, industry-specific frameworks, and standardization to overcome existing limitations.

Artificial Intelligence (AI) is increasingly impacting the environment, pedagogy, and management practices in engineering education. However, literature reviews that provide a systematic review and analysis of AI in engineering education remain limited. To address this research gap and gain a comprehensive understanding of AI applications in engineering education, this study conducts a systematic review based on 161 studies on AI applications in engineering education. Using VOSviewer, this paper analyzes the research hotspots of AI in engineering education, as well as the top ten organizations, countries, cited sources, and cited authors. Furthermore, the study categorizes the application of AI technologies in engineering education into seven distinct categories: Virtual Experiment Environments, Learning Prediction, Learning Analytics, Engineering Education Robots, Intelligent Tutoring Systems, Automatic Evaluation, and Assisted Learning. The study reveals that these technologies have already been widely adopted. Moreover, this paper summarizes the influence of AI technologies on engineering education, along with the implications and challenges, providing a foundation for further exploration of the integration of AI technologies into educational systems.

Planning and allocating public resources is essential because resources are always limited and must be sufficient to meet a country’s needs. Therefore, it is necessary to define how resources are distributed based on the amount collected, directly affecting society in the most diverse areas, such as education and health. Owing to advances in artificial intelligence in recent years, studies have been conducted to explore and propose intelligent solutions that enable the most diverse analyses in this critical area. Among these, data mining has emerged as a viable solution. Generally, data mining consists of three major steps: pre-processing, pattern extraction, and post-processing. Thus, to understand how data mining has been used in the most diverse subjects related to public planning and budgeting, this study presents systematic literature mapping. The aims were (i) to provide an overview of the aspects related to the data mining steps in the presented context and, (ii) to identify gaps that can be addressed and/or explored. The results are presented and discussed throughout this paper based on 30 papers selected over 10 years (from 2014 to 2023), with the potential to significantly impact future research and practice in public planning and data mining.

Abstract This paper surveys research and practice on enhancing peer assessment with artificial intelligence. Its objectives are to give the structure of the theoretical framework underpinning the study, synopsize a scoping review of the literature that illustrates this structure, and provide a case study which further illustrates this structure. The theoretical framework has six areas: (i) Assigning Peer Assessors, (ii) Enhancing Individual Reviews, (iii) Deriving Peer Grades/Feedback, (iv) Analyzing Student Feedback, (v) Facilitating Instructor Oversight and (vi) Peer Assessment Systems. The vast majority of the 79 papers in the review found that artificial intelligence improved peer assessment. However, the focus of many papers was on diversity in grades and feedback, fuzzy logic and the analysis of feedback with a view to equalizing its quality. Relatively few papers focused on automated assignment, automated assessment, calibration, teamwork effectiveness and automated feedback and these merit further research. This picture suggests AI is making inroads into peer assessment, but there is still a considerable way to go, particularly in the under-researched areas. The paper incorporates a case study of the RIPPLE peer-assessment tool, which harnesses student wisdom, insights from the learning sciences and AI to enable time-constrained educators to immerse their students in deep and personalized learning experiences that effectively prepare them to serve as assessors. Once trained, they use a comprehensive rubric to vet learning resources submitted by other students. They thereby create pools of high-quality learning resources which can be used to recommend personalized content to students. RIPPLE engages students in a trio of intertwined activities: creation, review and personalized practice, generating many resource types. AI-driven real-time feedback is given but students are counseled to assess whether it is accurate. Affordances and challenges for researchers and practitioners were identified.

This review paper focuses on Fiber-Reinforced Topology Optimization (FRTO) methods for automated manufacturing techniques, addressing topology and morphology optimization. Accordingly, the review introduces the main TO techniques and the common reinforcement path design strategies using concurrent and sequential optimization approaches. Furthermore, this paper examines the potential transformation of the conventional role of TO algorithms in structural optimization by integrating Artificial Intelligence (AI) into the optimization process [1]. We collected and categorized the most relevant papers from the past decade in the field of FRTO; comparisons were made based on appropriate metrics, including algorithm types, effectiveness, and validation environment. We emphasize practical considerations such as manufacturing constraints and algorithmic efficiency, addressing real-world usability aspects [2]. The analysis underscores the necessity for universally applicable benchmark methods and standardization to facilitate direct comparisons among various methodologies [3]. The main conclusions of the paper highlight the emerging trends in research, the potential of fiber-reinforced polymer composites designed by FRTO, the challenges facing the field, and the efficiency improvements and synergy with AI, indicating an evolving role for TO in structural optimization.

Abstract The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs. While ANNs are relatively mature to construct intrusion detection models, some challenges remain. Among the most notorious of these are the bloated models caused by the large number of parameters, and the non-interpretability of the models. Our paper presents Convolutional Kolmogorov-Arnold Networks (CKANs), which are designed to overcome these difficulties and provide an interpretable and accurate intrusion detection model. Kolmogorov-Arnold Networks (KANs) are developed from the Kolmogorov-Arnold representation theorem. Meanwhile, CKAN incorporates a convolutional computational mechanism based on KAN. The model proposed in this paper is constructed by incorporating attention mechanisms into CKAN’s computational logic. The datasets CICIoT2023 and CICIoMT2024 were used for model training and validation. From the results of evaluating the performance indicators of the experiments, the intrusion detection model constructed based on CKANs has an attractive application prospect. As compared with other methods, the model can predict a much higher level of accuracy with significantly fewer parameters. However, it is not superior in terms of memory usage, execution speed and energy consumption.

Abstract The advancement of artificial intelligence (AI) tools has revolutionized teaching and learning, particularly in healthcare education, where they enhance pedagogy, foster immersive learning, and support healthcare provision. However, their use in healthcare education is contentious, warranting careful examination, especially regarding Generative AI (GenAI) tools like ChatGPT. This scoping review aims to explore the impact of ChatGPT on healthcare education and identify future research directions. The scoping review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, utilized search terms such as “ChatGPT,” “GPT,” “natural language processing,” “large language models,” and “health education”. The review followed the five-stage framework outlined for organization and analysis. The search encompassed Web of Science (WOS) (n = 100), PubMed (n = 100), CINAHL (n = 21), SCOPUS (n = 4), Science Direct (n = 25), and Google Scholar (n = 150). Initially, 400 papers were retrieved from these search engines, which were then reviewed and narrowed down to 33 papers for final analysis. This review investigated the trends, challenges, and opportunities of ChatGPT in healthcare education. The findings suggest that GenAI tools such as ChatGPT can significantly enhance teaching, learning, and research in healthcare education. Developed countries, particularly the United States and China, which are leaders in AI investment and research, lead research on ChatGPT's applications in healthcare education, with limited studies conducted in the African region. Additionally, barriers remain that could lead to ethical and legal issues, particularly exacerbating inequalities in developing countries. Further research is needed to promote better GenAI practices in healthcare educational settings, especially for individuals in these regions.

DESIGNING AND ASSESSING WITH THE SUPPORT OF ARTIFICIAL INTELLIGENCE: ELEMENTS FOR A CRITICAL APPROACH TO THE USE OF CHATBOTS Abstract This paper explores the critical integration of artificial intelligence (AI), specifically focusing on using chatbots in training design and learning assessment, aiming to uncover both the potential and challenges in educational and training contexts. Through two exploratory empirical studies – one centered on the use of ChatGPT in training design and the other on its application in school learning assessments – the analysis examines the perceptions of teachers and students. The findings reveal that chatbots, such as ChatGPT, can significantly reduce the workload of teachers and future training designers, improve access to educational resources, and provide timely feedback. However, concerns emerge regarding technological dependency and superficial learning, with ethical and pedagogical implications that warrant a critical examination of the pedagogical effectiveness of AI tools. The paper concludes by proposing strategies for AI’s ethical and thoughtful integration in education, promoting a balance between technology and reflective, critical educational practice.

This paper surveys real-time object detection literature critically and analytically, focusing particularly on pedestrian detection for safe autonomous vehicles. It addresses the challenges in the domain, some of the sources of which are variations in age, gender, clothing, lighting, backgrounds, and occlusion. The paper reviews object detection algorithms after providing an overview of deep learning basics and main architectures of neural networks, followed by discussion on existing algorithms along with their strengths, weaknesses, and future research directions. There is a need for pedestrian detection datasets with further complex annotations and multi-source integration, which captures interactions between pedestrians and their surroundings. Incorporating advanced sensors, including LiDAR, infrared, and depth sensors, as the foremost means to enhance the detection capabilities in more adverse conditions, such as low-light situations and occlusion. However, architectures such as YOLO, SSD, and Faster R-CNN, which have led to current improvements in performance, still allow room for improving pedestrian detection accuracy. By filling in these insights and proposed solutions, the paper focus on the development of pedestrian detection technology, how it can be brought into a safer, reliable, real-world applicability towards the system of autonomous driving. All of these results point to continued innovation towards deep learning, multi-sensor integration, and developing datasets to achieve optimal performance levels in real world conditions for autonomous driving systems.

The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field is crucial for both academics and practitioners. This paper aims to conduct a comprehensive bibliometric study of the scientific literature on portfolio optimization, focusing on the impact of AI, ML, and DL advancements. By analyzing key trends, influential publications, and emerging research areas, this study provides valuable insights into the progression of portfolio optimization research in the context of these transformative technologies, helping to map future directions and identify knowledge gaps in the field. This paper endeavors to present an exhaustive synthesis of the most recent advancements and innovations within the domain of portfolio optimization, particularly as influenced by progressive developments in AI, ML and DL from 1996 to 2024. Employing a rigorous bibliometric analysis, this study scrutinizes the structural and global paradigms governing this field. The analytical framework integrates several dimensions, including: (1) comprehensive dataset interrogation, (2) critical evaluation of source repositories, (3) contributions of seminal authors, (4) geographical and institutional affiliations, (5) document-centric analysis, and (6) exploration of keyword dynamics. A corpus of 745 bibliographic entries, meticulously curated from the Web of Science database, forms the basis of this inquiry, which utilizes advanced Scientometric network methodologies to extrapolate substantive research insights. The discourse culminates in a robust critique of the inherent strengths and methodological limitations, while delineating strategic avenues for future research, with the objective of steering ongoing scholarly discourse in the realm of portfolio optimization. The empirical outcomes of this study enhance the understanding of prevailing intellectual trajectories, thus laying a fortified foundation for future investigative pursuits in this critically evolving discipline.

This paper will provide information on the impact of AI in daily life and work-related activities.Today, AI functionalities could nowadays transform businesses, playing a critical role in enhancing and improving decisions. From virtual assistants to automation tools, AI covers a great amount of information, which could impact the core. In this paper, the productivity and sense of failure of AI will be paper. The productivity of AI, such as, varies by tasks and industry. AI could excel in repetitive and high-precision tasks. On the other hand, humans outperform AI in tasks requiring creativity and emotional intelligence. This qualitative study will show the perception of integrating AI into workflows and asking questions about value added. To evaluate the impact of artificial intelligence (AI) on business operations, an online survey was conducted to examine perceptions of AI's efficiency, adaptability, and fault tolerance.The analysis revealed generational differences in acceptance and trust towards AI. Younger respondents, particularly those under 25, were found to have greater tolerance for AI errors and a greater willingness to integrate AI into workflows. This is likely to reflect their familiarity with technology. In contrast, older respondents exhibited lower levels of trust and acceptance, particularly in contexts requiring precision, such as financial transactions. The results suggest that while AI is perceived as highly effective in repetitive and data-intensive tasks, its limitations in adaptability and emotional intelligence remain a concern. The findings emohasize the need for reskilling initiatives to facilitate workforce transitions and the development of ethical guidelines to address trust and reliability issues.

The economic significance of artificial intelligence (AI) is rapidly increasing, influencing industries, employment, and consumer behaviour all around the globe. As AI applications become increasingly apparent and tangible in our daily lives, understanding consumer attitudes toward AI has become essential for businesses and policymakers aiming to drive adoption and trust in such technologies. This paper firstly explores the economic relevance of AI by highlighting its impact on various fields and its role in driving economic growth. A critical aspect of harnessing the full economic potential of AI lies in the accurate measurement of consumer attitudes, as public perception influences the adoption of technology, hence its final market success. Accurate insights into public attitudes are also key to shaping policies that ensure ethical AI integration, fostering a balanced approach between innovation and societal concerns. Beyond adoption, understanding attitudes helps identify potential barriers which could hinder the widespread acceptance of AI systems. This paper then proceeds to providing a critical overview of the different scales developed for assessing consumer attitudes towards AI. These scales have been established in varied contexts, from evaluating general perceptions to measuring attitudes toward specific AI applications. The review underscores the importance of ensuring adaptability and context-specific relevance when selecting or designing these tools. Comparisons between scales reveal distinct advantages and disadvantages in relation to reliability, robustness, contextual limitations or scope. Finally, this paper aims to provide perspectives for selecting the right AI attitude scale, emphasizing different methodological considerations. These insights aim to guide researchers and practitioners in effectively measuring consumer attitudes, contributing to more informed decisions in AI based innovative processes.