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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.

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]

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.

Sewer networks play a pivotal role in our everyday lives by transporting the stormwater and urban sewage away from the urban areas. In this regard, Sewer Overflow (SO) has been considered as a detrimental threat to our environment and health, which results from the wastewater discharge into the environment. In order to grapple with such deleterious phenomenon, numerous studies have been conducted; however, there has not been any review paper that provides the researchers undertaking research in this area with the following inclusive picture: (1) detailed-scientometric analysis of the research undertaken hitherto, (2) the types of methodologies used in the previous studies, (3) the aspects of environment impacted by the SO occurrence, and (4) the gaps existing in the relative literature together with the potential future works to be undertaken. Based on the comprehensive review undertaken, it is observed that simulation and artificial intelligence-based methods have been the most popular approaches. In addition, it has come to the attention that the detrimental impacts associated with the SO are fourfold as follows: air, quality of water, soil, and business and structure. Among these, the majority of the studies' focus have been tilted towards the impact of SO on the quality of ground water. The outcomes of this state-of-the-art review provides the researchers and environmental engineers with inclusive hindsight in dealing with such serious issue, which in turn, this culminates in a significant improvement in our environment as well as humans' well-beings., (Copyright © 2021. Published by Elsevier Ltd.)

Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. KEY POINTS: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined., (© 2021. The Author(s).)

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).

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths according to the World Health Organization (WHO). Not only affecting the lungs but also provoking acute respiratory distress, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is able to infect multiple cell types including cardiac and vascular cells. Hence a significant proportion of infected patients develop cardiac events, such as arrhythmias and heart failure. Patients with cardiovascular comorbidities are at highest risk of cardiac death. To face the pandemic and limit its burden, health authorities have launched several fast-track calls for research projects aiming to develop rapid strategies to combat the disease, as well as longer-term projects to prepare for the future. Biomarkers have the possibility to aid in clinical decision-making and tailoring healthcare in order to improve patient quality of life. The biomarker potential of circulating RNAs has been recognized in several disease conditions, including cardiovascular disease. RNA biomarkers may be useful in the current COVID-19 situation. The discovery, validation, and marketing of novel biomarkers, including RNA biomarkers, require multi-centre studies by large and interdisciplinary collaborative networks, involving both the academia and the industry. Here, members of the EU-CardioRNA COST Action CA17129 summarize the current knowledge about the strain that COVID-19 places on the cardiovascular system and discuss how RNA biomarkers can aid to limit this burden. They present the benefits and challenges of the discovery of novel RNA biomarkers, the need for networking efforts, and the added value of artificial intelligence to achieve reliable advances., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Aim: To discuss ethical issues related to a complex study (PROFID) involving the development of a new, partly artificial intelligence-based, prediction model to enable personalised decision-making about the implantation of an implantable cardioverter defibrillator (ICD) in postmyocardial infarction patients, and a parallel non-inferiority and superiority trial to test decision-making informed by that model., Method: The position expressed in this paper is based on an analysis of the PROFID trials using concepts from high-profile publications in the ethical literature., Results: We identify ethical issues related to the testing of the model in the treatment setting, and to both the superiority and the non-inferiority trial. We underline the need for ethical-empirical studies about these issues, also among patients, as a parallel to the actual trials. The number of ethics committees involved is an organisational, but also an ethical challenge., Conclusion: The PROFID trials, and probably other studies of similar scale and complexity, raise questions that deserve dedicated parallel ethics and social science research, but do not constitute a generic obstacle. A harmonisation procedure, comparable to the Voluntary Harmonization Procedure (VHP) for medication trials, could be needed for this type of trials., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories: study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective: database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting.Key Points• Machine learning is new and rather complex for the radiology community.• Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting.• Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community.