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There has been a policy push in K-12 educational settings towards personalized learning in the last decade. Commercial platforms and learning designers have responded, offering learning tools to support teaching and learning through data-driven insights and recommendations. Trending towards the augmentation or replacing human teachers with non-human technology, this paper argues that personalized learning with human teachers is an entirely different process from personalization with digital twins. Drawing on new materialist thinking, it explores the impacts and implications for discourse concerning teacher quality and disadvantages within educational systems. It clarifies the conflation of the terms "personalized learning" and "personalization" to illuminate the power, positionality, and privilege enabled for some, in conflating terms in Australian educational policy.

Artificial Intelligence in higher education (AIED) is becoming a more important research area with increasing developments and application of AI within the wider society. However, as yet AI based tools have not been widely adopted in higher education. As a result there is a lack of sound evidence available on the pedagogical impact of AI for learning and teaching. This conceptual paper thus seeks to bridge the gap and addresses the following question: is artificial intelligence really the new big thing that will revolutionise learning and teaching in higher education? Adopting the technological pedagogical content knowledge (TPACK) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical foundations, we argue that Artificial Intelligence (AI) technologies, at least in their current state of development, do not afford any real new advances for pedagogy in higher education. This is mainly because there does not seem to be valid evidence as to how the use of AI technologies and applications has helped students improve learning, and/or helped tutors make effective pedagogical changes. In addition, the pedagogical affordances of AI have not yet been clearly defined. The challenges that the higher education sector is currently experiencing relating to AI adoption are discussed at three hierarchical levels, namely national, institutional and personal levels. The paper ends with recommendations with regard to accelerating AI use in universities. This includes developing dedicated AI adoption strategies at the institutional level, updating the existing technology infrastructure and upskilling academic tutors for AI.

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.

The purpose of this study was to investigate the impact of mLearning or mobile device practice on the attention and accuracy of student's use of math concepts, specifically, telling time. A single subject, alternating treatment design was used to compare mLearning to paper and pencil practice in four 3rd grade male students. Results were mixed; two children were observed to be more on-task during the mLearning practice, and two children were observed to perform similarly across both conditions. Additionally, two children performed similarly on correctly completed problems across both conditions, and two children performed better using paper and pencil practice. All students completed more math problems during the paper and pencil practice.

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.

Artificial intelligence tools have presented many challenges and opportunities to transform teaching and learning on college campuses. These changes are significant enough to require colleges to take action to create a framework by which faculty and students can navigate the proper usage of these tools. Rather than working to create entirely new policies strictly for addressing these new technologies, they should instead edit their existing academic integrity and intellectual property statements to incorporate explicit mentions of artificial intelligence.

This paper focuses on the opportunities and challenges associated with the use of artificial intelligence (AI) in academic library operations. In the quest to render fast, effective and efficient services, academic libraries have adopted different technologies in the past. Artificial intelligence technologies is the latest among the technologies currently being introduced in libraries. The technology which is considered an intelligent system, come in the form of robots and expert systems which have natural language processing, machine learning and pattern recognition capabilities. This paper examined the features of AI, the application of AI to library operations, examples of academic libraries with AI technologies in Sub-Saharan Africa, the need for AI in libraries and the challenges associated with the adoption of AI in libraries. The study concluded that AI holds a lot of prospects for the improvement of information services delivery in African academic libraries. Consequently, its adoption is a sinequanon to delivering robust library services in the Fourth Industrial Revolution (4IR).

Background: In previous decades, large-scale research has been carried out on bacterial meningitis. In every field, citation analysis is the most significant contribution. The study's objective was to identify and analyze the 100 articles on bacterial meningitis that received the most citations between 2000 and 2023, highlighting the most significant developments in the field. Objective: The objective of this study was to find out what makes a highly influential article by identifying and analyzing the characteristics of the 100 articles in the field of bacterial meningitis that receive the most citations. The goal of this study was to find and examine the 100 articles on bacterial meningitis that received the most citations. Methodology: We identified the top 100 most-cited papers in the field of bacterial meningitis from 55 journals using the Dimensions AI database. The results of each author's analysis of 100 articles were then compared. We gathered fundamental data such as the journal's title, country of publication, and study type. Descriptive counts or percentages were used to compare the various categories. Results: Between the year 2000 and the year 2023, articles were published. The total number of citations ranged from 115 to 1176, with 42 papers receiving more than 200 citations. In 2008, 14 articles were published, followed by 10 in 2000 and 2007. One thousand one hundred and seventy-six times were given to the most-cited paper, whereas 115 times were given to the least-cited article. "Clinical Features and Prognostic Factors in Adults with Bacterial Meningitis," by Diederik van de Beek, et al. (2004) was the article that received the most citations. 1176 people have cited this article. van de Beek Diederik of the Academic Medical Center in The Netherlands is the author who has written the most articles, was mentioned in 14 of the top 100 articles. Papers were primarily published in Pediatrics (n = 9) publication with 1861 citations. The Netherlands came in second with 18 publications, followed by the United States (n = 46). Conclusion: Our study uses bibliometrics and visualization analysis of the most important articles in this field to show the current state of research in the area of bacterial meningitis, provide a history of research trends, and offer a perspective for future bacterial predicts the growth of meningitis. [ABSTRACT FROM AUTHOR]

Extended reality (XR) enables both new opportunities but also introduces new barriers for inclusion in society. Furthermore, XR is less researched than web, desktop and mobile applications. This position paper presents the concept of an XR platform for inclusion, with the purpose to make people on the autism spectrum and with other disabilities, more independent of help from others in everyday life situations. Based on previous research, our position is that, through current and future XR technologies combined with civic and artificial intelligence, it is possible to create individually personalised support for this purpose, grounded in practice to ensure validation. [ABSTRACT FROM AUTHOR]

In this era of knowledge, academic researchers are growing every day, this also spikes a growth in published literature on the new innovations and findings. This leads to a problem where the reviewers at the conferences must go through many research papers to determine whether they are suitable for the conference or not. This problem has caused the necessity of an effective paper screening tool for optimizing the literature review process. This research presents a development of a new Paper Screening Tool (PST) aimed at increasing the efficiency and accuracy of the literature screening phase. Leveraging the NPL processing techniques this PST and reduces a lot of manual efforts. Through comprehensive evaluation using a diverse dataset, the tools provide high precision. The PST also has user friendly interfaces and customizable report generation which empowers the researchers screening process to their specific needs. This paper contributes to literature by solving the challenge of information overloading during the literature review. [ABSTRACT FROM AUTHOR]

Digital cytology and artificial intelligence (AI) are gaining greater adoption in the cytology laboratory. However, peer-reviewed real-world data and literature are lacking in regard to the current clinical landscape. The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology. The aim of the group was to investigate the feasibility of incorporating digital cytology, specifically cytology whole slide scanning and AI applications, into the workflow of the laboratory. In turn, the impact on cytopathologists, cytologists (cytotechnologists), and cytology departments were also assessed. The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and AI with multiple industry corporate representatives. This white paper, presented in 2 parts, summarizes the current state of digital cytology and AI practice in global cytology practice. Part 1 of the white paper is presented as a separate paper which details a review and best practice recommendations for incorporating digital cytology into practice. Part 2 of the white paper presented here provides a comprehensive review of AI in cytology practice along with best practice recommendations and legal considerations. Additionally, the cytology global survey results highlighting current AI practices by various laboratories, as well as current attitudes, are reported. • The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology to investigate the feasibility of incorporating digital cytology and artificial intelligence applications into the workflow of the laboratory. • The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and artificial intelligence (AI) with multiple industry corporate representatives. • Part 2 of the white paper presented here provides a comprehensive review of artificial intelligence in cytology practice along with best practice recommendations and legal considerations. [ABSTRACT FROM AUTHOR]

Radiography education is pivotal in training skilled radiographers for diagnostic imaging and therapeutic applications. With technological advancements, interest in innovative educational tools to enhance traditional teaching methods is growing. This discussion paper explores the possibility of the integration of ChatGPT, a cutting-edge conversational AI language model, into radiography education. We report that ChatGPT offers interactive learning opportunities that can facilitate learning. It also provides self-paced learning, revision platforms, and supports educators in scenario creation, assessment development, group collaboration, and professional and research activities. Despite these benefits, it is important to carefully consider issues related to academic integrity and privacy, along with the opportunities and challenges presented by this new technology in radiography education. This paper highlights some of the prospects and limitations of the potential applications of ChatGPT in radiography education, underscoring the benefits for both students and educators. However, its implementation must be considered thoughtfully and ethically, taking into account its strengths and limitations. Integrating ChatGPT in radiography education has the potential to improve radiography education by improving digital literacy and graduate outcomes of students while streamlining the preparation process for educators. However, ethical implementation is vital for optimal outcomes. [ABSTRACT FROM AUTHOR]

The diversity and advance of information, communication, and analytical technologies and their increasing adoption to assist instruction and learning give rise to various technology-driven conferences (e.g., artificial intelligence in education) in educational technology. Previous reviews on educational technology commonly focused on journal articles while seldom including mainstream conference papers which also contribute to an important part of scientific output in computer science and emerging disciplines like educational technology and are equally and even more important than articles in knowledge transmission. Hence, conference papers should also be included in bibliometric studies to produce a complete and precise picture of scientific production concerning educational technology. This study, therefore, uses bibliometrics and topic modeling to analyze papers from mainstream conferences, including Artificial Intelligence in Education, Learning Analytics and Knowledge, Educational Data Mining, Intelligent Tutoring System, and Learning at Scale, focusing on contributors, collaborations, and particularly research topics and topic evolutions to inform relevant stakeholders about educational technology's development and its future. Results indicate promising areas like affective computing and behavior mining for adaptive instruction, recommender systems in personalized learning recommendations, eye-tracking for cognitive process diagnosis, videos for feedback provision, and natural language processing in discourse analysis and language education.

The emergence of generative artificial intelligence such as ChatGPT has left people feeling ambivalent and disagreement among scholars, academicians, educators and the community at large prevails. While the artificial intelligence could potentially revolutionize how research is conducted and how research papers are written, a number of ethical concerns arise. In particular, the world of academia has reservations pertaining to whether this language model will actually do more good than harm, especially as far as academic writing is concerned. This paper argues that the cutting-edge technology is here to stay and the question is not whether to accept it, but rather, how to best utilize it judiciously, cautiously and responsibly to improve research performance by strictly adhering to academic integrity and transparency. Potential benefits and drawbacks of ChatGPT will be critically examined in light of current literature and, when relevant, potential solutions to the drawbacks will also be provided or commented on. Needless to say, the use of artificial intelligence in academic writing is still in its infancy and more discussion and debates pertaining to its use and merit are highly urged. This paper contributes to these on-going debates.

This study investigates the mechanism by which knowledge spilled over from a firm's research publication consequently spills into the focal firm as a form of proprietary knowledge when it is engaged in an emerging science-related technology. We define the knowledge spillover pool (KSP) as an evolving group of papers citing a paper published by a firm. Focusing on the recent development of artificial intelligence, on which firms have published actively, we compare the KSP conditions related to the increase in patents created by the focal firm with those created by external actors. Using a Cox regression and subsequent contrast test, we find that both an increasing KSP and an increasing similarity between the idea published by the focal firm and KSP are positively related to the proprietary knowledge creation of both the focal firm and external actors, with such relations being significantly stronger for the focal firm than for external actors. On the contrary, an increasing proportion of industry papers in the KSP are positively associated with the proprietary knowledge creation not only by the focal firm but also by external actors to a similar degree. We contribute to the literature on selective revealing and to the firms' publishing strategies. [ABSTRACT FROM AUTHOR]

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.

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.

This study delves into the opportunities and challenges associated with the deployment of AI tools in the education sector. It systematically explores the potential benefits and risks inherent in utilizing these tools while specifically addressing the complexities of identifying and preventing academic dishonesty. Recognizing the ethical dimensions, the paper further outlines strategies that educational institutions can adopt to ensure the ethical and responsible use of AI tools. Emphasizing a proactive stance, the paper suggests that by implementing these strategies, schools can harness the benefits of AI tools while mitigating the risks associated with potential misuse. As the adoption of AI tools in education continues to expand, all stakeholders must stay abreast of the latest developments in the field. This knowledge equips educators to navigate the opportunities and challenges posed by AI tools, fostering a learning environment that is both secure and conducive to empowering students to realize their full potential.

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.

The academic community has witnessed a notable increase in paper publications, whereby the rapid pace at which modern society seeks information underscores the critical need for literature mapping. This study introduces an innovative automatic model for categorizing articles by subject matter using Machine Learning (ML) algorithms for classification and category labeling, alongside a proposed ranking method called SSS (Scientific Significance Score) and using Z-score to select the finest papers. This paper's use case concerns industry productivity. The key findings include the following: (1) The Decision Tree model demonstrated superior performance with an accuracy rate of 75% in classifying articles within the productivity and industry theme. (2) Through a ranking methodology based on citation count and publication date, it identified the finest papers. (3) Recent publications with higher citation counts achieved better scores. (4) The model's sensitivity to outliers underscores the importance of addressing database imbalances, necessitating caution during training by excluding biased categories. These findings not only advance the utilization of ML models for paper classification but also lay a foundation for further research into productivity within the industry, exploring themes such as artificial intelligence, efficiency, industry 4.0, innovation, and sustainability. [ABSTRACT FROM AUTHOR]

The notable increase in retraction papers has attracted considerable attention from diverse stakeholders. Various sources are now offering information related to research integrity, including concerns voiced on social media, disclosed lists of paper mills, and retraction notices accessible through journal websites. However, despite the availability of such resources, there remains a lack of a unified platform to consolidate this information, thereby hindering efficient searching and cross-referencing. Thus, it is imperative to develop a comprehensive platform for retracted papers and related concerns. This article aims to introduce "Amend," a platform designed to integrate information on research integrity from diverse sources. The Amend platform consolidates concerns and lists of problematic articles sourced from social media platforms (e.g., PubPeer, For Better Science), retraction notices from journal websites, and citation databases (e.g., Web of Science, CrossRef). Moreover, Amend includes investigation and punishment announcements released by administrative agencies (e.g., NSFC, MOE, MOST, CAS). Each related paper is marked and can be traced back to its information source via a provided link. Furthermore, the Amend database incorporates various attributes of retracted articles, including citation topics, funding details, open access status, and more. The reasons for retraction are identified and classified as either academic misconduct or honest errors, with detailed subcategories provided for further clarity. Within the Amend platform, a total of 32,515 retracted papers indexed in SCI, SSCI, and ESCI between 1980 and 2023 were identified. Of these, 26,620 (81.87%) were associated with academic misconduct. The retraction rate stands at 6.64 per 10,000 articles. Notably, the retraction rate for non-gold open access articles significantly differs from that for gold open access articles, with this disparity progressively widening over the years. Furthermore, the reasons for retractions have shifted from traditional individual behaviors like falsification, fabrication, plagiarism, and duplication to more organized large-scale fraudulent practices, including Paper Mills, Fake Peer-review, and Artificial Intelligence Generated Content (AIGC). The Amend platform may not fully capture all retracted and concerning papers, thereby impacting its comprehensiveness. Additionally, inaccuracies in retraction notices may lead to errors in tagged reasons. Amend provides an integrated platform for stakeholders to enhance monitoring, analysis, and research on academic misconduct issues. Ultimately, the Amend database can contribute to upholding scientific integrity. This study introduces a globally integrated platform for retracted and concerning papers, along with a preliminary analysis of the evolutionary trends in retracted papers. [ABSTRACT FROM AUTHOR]

This position paper introduces the idea of cognicy, the foundational ability to think and understand in a process that decouples cognitive processes from their tangible outcomes. Generative artificial intelligence (AI) can produce output often nearly indistinguishable from a human product, which presents a problem for educational assessment. Cognicy focuses on the process of thought, which is uniquely human, rather than the output, which can be machine generated. The nearest parallel is numeracy, which decouples the underlying mathematical concept from the task of calculation. Similarly, cognicy seeks to disentangle the essential thought process from the outputs, which now can be easily composed by AI. Cognicy is thus a tool for shifting the way in which higher education views the intersection of generative artificial intelligence, learning, and evaluation. It must be where future frameworks for learning focus. Process must be seen as separate from product so that human skills and learning stay relevant. This paper gives a name to these human-based, AI adjacent skills, creating a shared language to begin larger discussions. As a means of starting the conversation, the paper explores the relationship of cognicy to the concepts of Universal Design for Learning (UDL), metacognition, and AI literacy to show how this emerging framework might be employed.

Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the implications of (1) a recent case related to the General Data Protection Regulation (GDPR) and (2) the release of ChatGPT, a generative AI model capable to producing 'human-like' text. These events raise questions for the future of AIEd and the underlying function of assessment, and highlight the importance of active student participation in the integration of AI in education. [This article has been presented in the 5th International Open & Distance Learning Conference-IODL 2022.]

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. [ABSTRACT FROM AUTHOR]

Artificial intelligence and machine learning technologies have a variety of applications throughout the provision of health care, such as clinical documentation, diagnostic image processing, and clinical decision support. This position paper describes the American College of Physicians' (ACP) foundational positions and recommendations regarding the use of these technologies in the provision of health care. The foundation of these positions are principles in the ACP Ethics Manual. Internal medicine physicians are increasingly interacting with systems that implement artificial intelligence (AI) and machine learning (ML) technologies. Some physicians and health care systems are even developing their own AI models, both within and outside of electronic health record (EHR) systems. These technologies have various applications throughout the provision of health care, such as clinical documentation, diagnostic image processing, and clinical decision support. With the growing availability of vast amounts of patient data and unprecedented levels of clinician burnout, the proliferation of these technologies is cautiously welcomed by some physicians. Others think it presents challenges to the patient–physician relationship and the professional integrity of physicians. These dispositions are understandable, given the "black box" nature of some AI models, for which specifications and development methods can be closely guarded or proprietary, along with the relative lagging or absence of appropriate regulatory scrutiny and validation. This American College of Physicians (ACP) position paper describes the College's foundational positions and recommendations regarding the use of AI- and ML-enabled tools and systems in the provision of health care. Many of the College's positions and recommendations, such as those related to patient-centeredness, privacy, and transparency, are founded on principles in the ACP Ethics Manual. They are also derived from considerations for the clinical safety and effectiveness of the tools as well as their potential consequences regarding health disparities. The College calls for more research on the clinical and ethical implications of these technologies and their effects on patient health and well-being. [ABSTRACT FROM AUTHOR]

Paper mills, companies that write scientific papers and gain acceptance for them, then sell authorships of these papers, present a key challenge in medicine and other healthcare fields. This challenge is becoming more acute with artificial intelligence (AI), where AI writes the manuscripts and then the paper mills sell the authorships of these papers. The aim of the current research is to provide a method for detecting fake papers. The method reported in this article uses a machine learning approach to create decision trees to identify fake papers. The data were collected from Web of Science and multiple journals in various fields. The article presents a method to identify fake papers based on the results of decision trees. Use of this method in a case study indicated its effectiveness in identifying a fake paper. This method to identify fake papers is applicable for authors, editors, and publishers across fields to investigate a single paper or to conduct an analysis of a group of manuscripts. Clinicians and others can use this method to evaluate articles they find in a search to ensure they are not fake articles and instead report actual research that was peer reviewed prior to publication in a journal. [ABSTRACT FROM AUTHOR]

This column will help the voice teacher better understand and evaluate voice research publications. Three main topics are discussed: 1) the basic structure of a scientific paper, 2) a method for quickly reading and understanding scientific papers, and 3) a method for evaluating voice science publications. A fictious ChatGPT-generated study is critiqued with the authors' method. [ABSTRACT FROM AUTHOR]

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.

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.

Big data refers to extremely large data generated at high volume, velocity, variety, and veracity. The nurse scientist is uniquely positioned to leverage big data to suggest novel hypotheses on patient care and the healthcare system. The purpose of this paper is to provide an introductory guide to understanding the use and capability of big data for nurse scientists. Herein, we discuss the practical, ethical, social, and educational implications of using big data in nursing research. Some practical challenges with the use of big data include data accessibility, data quality, missing data, variable data standards, fragmentation of health data, and software considerations. Opposing ethical positions arise with the use of big data, and arguments for and against the use of big data are underpinned by concerns about confidentiality, anonymity, and autonomy. The use of big data has health equity dimensions and addressing equity in data is an ethical imperative. There is a need to incorporate competencies needed to leverage big data for nursing research into advanced nursing educational curricula. Nursing science has a great opportunity to evolve and embrace the potential of big data. Nurse scientists should not be spectators but collaborators and drivers of policy change to better leverage and harness the potential of big data. [ABSTRACT FROM AUTHOR]

Evaluating academic papers and groups is important in scholar evaluation and literature retrieval. However, current evaluation indices, which pay excessive attention to the citation number rather than the citation importance and unidirectionality, are relatively simple. This study proposes new evaluation indices for papers and groups. First, an improved PageRank (PR) algorithm introducing citation importance is proposed to obtain a new citation-based paper index (CPI) via a pre-ranking and fine-tuning strategy. Second, to evaluate the paper's influence inside and outside its research field, the focus citation-based paper index (FCPI) and diversity citation-based paper index (DCPI) are proposed based on topic similarity and diversity, respectively. Third, aside from the statistical indices for academic papers, we propose a foreign academic degree of dependence (FAD) to characterise the dependence between two academic groups. Finally, artificial intelligence (AI) papers from 2005 to 2019 are utilised for a case study. [ABSTRACT FROM AUTHOR]

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.