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The rapid advances in Generative AI tools have produced both excitement and worry about how AI will impact academic writing. However, little is known about what norms are emerging around AI use in manuscript preparation or how these norms might be enforced. We address both gaps in the literature by conducting a survey of 271 academics about whether it is necessary to report ChatGPT use in manuscript preparation and by running GPT-modified abstracts from 2,716 published papers through a leading AI detection software to see if these detectors can detect different AI uses in manuscript preparation. We find that most academics do not think that using ChatGPT to fix grammar needs to be reported, but detection software did not always draw this distinction, as abstracts for which GPT was used to fix grammar were often flagged as having a high chance of being written by AI. We also find disagreements among academics on whether more substantial use of ChatGPT to rewrite text needs to be reported, and these differences were related to perceptions of ethics, academic role, and English language background. Finally, we found little difference in their perceptions about reporting ChatGPT and research assistant help, but significant differences in reporting perceptions between these sources of assistance and paid proofreading and other AI assistant tools (Grammarly and Word). Our results suggest that there might be challenges in getting authors to report AI use in manuscript preparation because (i) there is not uniform agreement about what uses of AI should be reported and (ii) journals might have trouble enforcing nuanced reporting requirements using AI detection tools., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Chemaya, Martin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Creativity is core to being human. Generative artificial intelligence (AI)-including powerful large language models (LLMs)-holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: With generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practitioners interested in bolstering creativity.

Background: Academic paper writing holds significant importance in the education of medical students, and poses a clear challenge for those whose first language is not English. This study aims to investigate the effectiveness of employing large language models, particularly ChatGPT, in improving the English academic writing skills of these students., Methods: A cohort of 25 third-year medical students from China was recruited. The study consisted of two stages. Firstly, the students were asked to write a mini paper. Secondly, the students were asked to revise the mini paper using ChatGPT within two weeks. The evaluation of the mini papers focused on three key dimensions, including structure, logic, and language. The evaluation method incorporated both manual scoring and AI scoring utilizing the ChatGPT-3.5 and ChatGPT-4 models. Additionally, we employed a questionnaire to gather feedback on students' experience in using ChatGPT., Results: After implementing ChatGPT for writing assistance, there was a notable increase in manual scoring by 4.23 points. Similarly, AI scoring based on the ChatGPT-3.5 model showed an increase of 4.82 points, while the ChatGPT-4 model showed an increase of 3.84 points. These results highlight the potential of large language models in supporting academic writing. Statistical analysis revealed no significant difference between manual scoring and ChatGPT-4 scoring, indicating the potential of ChatGPT-4 to assist teachers in the grading process. Feedback from the questionnaire indicated a generally positive response from students, with 92% acknowledging an improvement in the quality of their writing, 84% noting advancements in their language skills, and 76% recognizing the contribution of ChatGPT in supporting academic research., Conclusion: The study highlighted the efficacy of large language models like ChatGPT in augmenting the English academic writing proficiency of non-native speakers in medical education. Furthermore, it illustrated the potential of these models to make a contribution to the educational evaluation process, particularly in environments where English is not the primary language., (© 2024. The Author(s).)

Plain Language Summary: In this article, the author reflects upon recent advancements in artificial intelligence (AI) technologies have led to profound discussions about AI's role in scientific research and education. AI technologies are now capable of summarizing and analyzing large volumes of data, creating presentations, and even drafting parts of scientific papers and grants with minimal human input. With their exponential ongoing advancements, they will become even more capable in doing these tasks. As boundaries between humans and AI blurs, serious questions arise about the future roles, responsibilities, and identity of academic researchers, intellectual property, publishing and grantsmanship, and equity in the world of AI potentiated research., (Copyright © 2024 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)

Students' reflective writings in gross anatomy provide a rich source of complex emotions experienced by learners. However, qualitative approaches to evaluating student writings are resource heavy and timely. To overcome this, natural language processing, a nascent field of artificial intelligence that uses computational techniques for the analysis and synthesis of text, was used to compare health professional students' reflections on the importance of various regions of the body to their own lives and those of the anatomical donor dissected. A total of 1365 anonymous writings (677 about a donor, 688 about self) were collected from 132 students. Binary and trinary sentiment analysis was performed, as well as emotion detection using the National Research Council Emotion Lexicon which classified text into eight emotions: anger, fear, sadness, disgust, surprise, anticipation, trust, and joy. The most commonly written about body regions were the hands, heart, and brain. The reflections had an overwhelming positive sentiment with major contributing words "love" and "loved." Predominant words such as "pain" contributed to the negative sentiments and reflected various ailments experienced by students and revealed through dissections of the donors. The top three emotions were trust, joy, and anticipation. Each body region evoked a unique combination of emotions. Similarities between student self-reflections and reflections about their donor were evident suggesting a shared view of humanization and person centeredness. Given the pervasiveness of reflections in anatomy, adopting a natural language processing approach to analysis could provide a rich source of new information related to students' previously undiscovered experiences and competencies., (© 2023 The Authors. Anatomical Sciences Education published by Wiley Periodicals LLC on behalf of American Association for Anatomy.)

This article presents the use of an artificial intelligence (AI) tool (ChatGPT) to transform a didactic lecture to an interactive workshop. The workshop on scientific and scholarly report writing was conducted for preclinical medical students. The functions of the AI as teaching assistance and documentation were expected to support the workshop. In this workshop, the AI was used to summarise and generate the summary notes based on students' inputs throughout the interactive activities during the workshop. Future research should focus on potential functions of AI in medical education and its impacts on student's learning outcomes.

The current academic debate on the use of artificial intelligence (AI) in research and teaching has been ongoing since the launch of ChatGPT in November 2022. It mainly focuses on ethical considerations, academic integrity, authorship and the need for new legal frameworks. Time efficiencies may allow for more critical thinking, while ease of pattern recognition across large amounts of data may promote drug discovery, better clinical decision making and guideline development with resultant consequences for patient safety. AI is also prompting a re-evaluation of the nature of learning and the purpose of education worldwide. It challenges traditional pedagogies, forcing a shift from rote learning to more critical, analytical, and creative thinking skills. Despite this opportunity to re-think education concepts for pharmacy curricula several universities around the world have banned its use. This commentary summarizes the existing debate and identifies the consequences and opportunities for clinical pharmacy research and education., (© 2024. The Author(s).)

Purpose: To evaluate the influence of ChatGPT on academic tasks performed by undergraduate dental students., Method: Fifty-five participants completed scientific writing assignments. First, ChatGPT was utilized; subsequently, a conventional method involving the search of scientific articles was employed. Each task was preceded by a 30-min training session. The assignments were reviewed by professors, and an anonymous questionnaire was administered to the students regarding the usefulness of ChatGPT. Data were analyzed by Mann-Whitney U-test., Results: Final scores and scores for the criteria of utilization of evidence, evaluation of arguments, and generation of alternatives achieved higher values through the traditional method than with ChatGPT (p = 0.019, 0.042, 0.017, and <0.001, respectively). No differences were found between the two methods for the remaining criteria (p > 0.05). A total of 64.29% of the students found ChatGPT useful, 33.33% found it very useful, and 3.38% not very useful. Regarding its application in further academic activities, 54.76% considered it useful, 40.48% found it very useful, and 4.76% not very useful. A total of 61.90% of the participants indicated that ChatGPT contributed to over 25% of their productivity, while 11.9% perceived it contributed to less than 15%. Concerning the relevance of having known ChatGPT for academic tasks, 50% found it opportune, 45.24% found it very opportune, 2.38% were unsure, and the same percentage thought it is inopportune. All students provided positive feedback., Conclusion: Dental students highly valued the experience of using ChatGPT for academic tasks. Nonetheless, the traditional method of searching for scientific articles yield higher scores., (© 2024 American Dental Education Association.)

Objective: To analyse and compare the assessment and grading of human-written and machine-written formative essays., Study Design: Quasi-experimental, qualitative cross-sectional study. Place and Duration of the Study: Department of Science of Dental Materials, Hamdard College of Medicine & Dentistry, Hamdard University, Karachi, from February to April 2023., Methodology: Ten short formative essays of final-year dental students were manually assessed and graded. These essays were then graded using ChatGPT version 3.5. The chatbot responses and prompts were recorded and matched with manually graded essays. Qualitative analysis of the chatbot responses was then performed., Results: Four different prompts were given to the artificial intelligence (AI) driven platform of ChatGPT to grade the summative essays. These were the chatbot's initial responses without grading, the chatbot's response to grading against criteria, the chatbot's response to criteria-wise grading, and the chatbot's response to questions for the difference in grading. Based on the results, four innovative ways of using AI and machine learning (ML) have been proposed for medical educators: Automated grading, content analysis, plagiarism detection, and formative assessment. ChatGPT provided a comprehensive report with feedback on writing skills, as opposed to manual grading of essays., Conclusion: The chatbot's responses were fascinating and thought-provoking. AI and ML technologies can potentially supplement human grading in the assessment of essays. Medical educators need to embrace AI and ML technology to enhance the standards and quality of medical education, particularly when assessing long and short essay-type questions. Further empirical research and evaluation are needed to confirm their effectiveness., Key Words: Machine learning, Artificial intelligence, Essays, ChatGPT, Formative assessment.

Background: Large language models (LLMs) have gained prominence since the release of ChatGPT in late 2022., Objective: The aim of this study was to assess the accuracy of citations and references generated by ChatGPT (GPT-3.5) in two distinct academic domains: the natural sciences and humanities., Methods: Two researchers independently prompted ChatGPT to write an introduction section for a manuscript and include citations; they then evaluated the accuracy of the citations and Digital Object Identifiers (DOIs). Results were compared between the two disciplines., Results: Ten topics were included, including 5 in the natural sciences and 5 in the humanities. A total of 102 citations were generated, with 55 in the natural sciences and 47 in the humanities. Among these, 40 citations (72.7%) in the natural sciences and 36 citations (76.6%) in the humanities were confirmed to exist (P=.42). There were significant disparities found in DOI presence in the natural sciences (39/55, 70.9%) and the humanities (18/47, 38.3%), along with significant differences in accuracy between the two disciplines (18/55, 32.7% vs 4/47, 8.5%). DOI hallucination was more prevalent in the humanities (42/55, 89.4%). The Levenshtein distance was significantly higher in the humanities than in the natural sciences, reflecting the lower DOI accuracy., Conclusions: ChatGPT's performance in generating citations and references varies across disciplines. Differences in DOI standards and disciplinary nuances contribute to performance variations. Researchers should consider the strengths and limitations of artificial intelligence writing tools with respect to citation accuracy. The use of domain-specific models may enhance accuracy., (©Joseph Mugaanyi, Liuying Cai, Sumei Cheng, Caide Lu, Jing Huang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.04.2024.)

Objective: Large language models (LLMs) such as ChatGPT-4 have raised critical questions regarding their distinguishability from human-generated content. In this research, we evaluated the effectiveness of online detection tools in identifying ChatGPT-4 vs human-written text., Methods: A two texts produced by ChatGPT-4 using differing prompts and one text created by a human author were analytically assessed using the following online detection tools: GPTZero, ZeroGPT, Writer ACD, and Originality., Results: The findings revealed a notable variance in the detection capabilities of the employed detection tools. GPTZero and ZeroGPT exhibited inconsistent assessments regarding the AI-origin of the texts. Writer ACD predominantly identified texts as human-written, whereas Originality consistently recognized the AI-generated content in both samples from ChatGPT-4. This highlights Originality's enhanced sensitivity to patterns characteristic of AI-generated text., Conclusion: The study demonstrates that while automatic detection tools may discern texts generated by ChatGPT-4 significant variability exists in their accuracy. Undoubtedly, there is an urgent need for advanced detection tools to ensure the authenticity and integrity of content, especially in scientific and academic research. However, our findings underscore an urgent need for more refined detection methodologies to prevent the misdetection of human-written content as AI-generated and vice versa.

The integration of generative artificial intelligence (AI) into academic research writing has revolutionized the field, offering powerful tools like ChatGPT and Bard to aid researchers in content generation and idea enhancement. We explore the current state of transparency regarding generative AI use in nursing academic research journals, emphasizing the need for explicitly declaring the use of generative AI by authors in the manuscript. Out of 125 nursing studies journals, 37.6% required explicit statements about generative AI use in their authors' guidelines. No significant differences in impact factors or journal categories were found between journals with and without such requirement. A similar evaluation of medicine, general and internal journals showed a lower percentage (14.5%) including the information about generative AI usage. Declaring generative AI tool usage is crucial for maintaining the transparency and credibility in academic writing. Additionally, extending the requirement for AI usage declarations to journal reviewers can enhance the quality of peer review and combat predatory journals in the academic publishing landscape. Our study highlights the need for active participation from nursing researchers in discussions surrounding standardization of generative AI declaration in academic research writing., (© 2023 The Authors. Journal of Nursing Scholarship published by Wiley Periodicals LLC on behalf of Sigma Theta Tau International.)

Purpose of Review: Three review articles have been written that discuss the roles of the central and peripheral nervous systems in fracture healing. While content among the articles is overlapping, there is a key difference between them: the use of artificial intelligence (AI). In one paper, the first draft was written solely by humans. In the second paper, the first draft was written solely by AI using ChatGPT 4.0 (AI-only or AIO). In the third paper, the first draft was written using ChatGPT 4.0 but the literature references were supplied from the human-written paper (AI-assisted or AIA). This project was done to evaluate the capacity of AI to conduct scientific writing. Importantly, all manuscripts were fact checked and extensively edited by all co-authors rendering the final manuscript drafts significantly different from the first drafts., Recent Findings: Unsurprisingly, the use of AI decreased the time spent to write a review. The two AI-written reviews took less time to write than the human-written paper; however, the changes and editing required in all three manuscripts were extensive. The human-written paper was edited the most. On the other hand, the AI-only paper was the most inaccurate with inappropriate reference usage and the AI-assisted paper had the greatest incidence of plagiarism. These findings show that each style of writing presents its own unique set of challenges and advantages. While AI can theoretically write scientific reviews, from these findings, the extent of editing done subsequently, the inaccuracy of the claims it makes, and the plagiarism by AI are all factors to be considered and a primary reason why it may be several years into the future before AI can present itself as a viable alternative for traditional scientific writing., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Purpose of Review: With the recent explosion in the use of artificial intelligence (AI) and specifically ChatGPT, we sought to determine whether ChatGPT could be used to assist in writing credible, peer-reviewed, scientific review articles. We also sought to assess, in a scientific study, the advantages and limitations of using ChatGPT for this purpose. To accomplish this, 3 topics of importance in musculoskeletal research were selected: (1) the intersection of Alzheimer's disease and bone; (2) the neural regulation of fracture healing; and (3) COVID-19 and musculoskeletal health. For each of these topics, 3 approaches to write manuscript drafts were undertaken: (1) human only; (2) ChatGPT only (AI-only); and (3) combination approach of #1 and #2 (AI-assisted). Articles were extensively fact checked and edited to ensure scientific quality, resulting in final manuscripts that were significantly different from the original drafts. Numerous parameters were measured throughout the process to quantitate advantages and disadvantages of approaches., Recent Findings: Overall, use of AI decreased the time spent to write the review article, but required more extensive fact checking. With the AI-only approach, up to 70% of the references cited were found to be inaccurate. Interestingly, the AI-assisted approach resulted in the highest similarity indices suggesting a higher likelihood of plagiarism. Finally, although the technology is rapidly changing, at the time of study, ChatGPT 4.0 had a cutoff date of September 2021 rendering identification of recent articles impossible. Therefore, all literature published past the cutoff date was manually provided to ChatGPT, rendering approaches #2 and #3 identical for contemporary citations. As a result, for the COVID-19 and musculoskeletal health topic, approach #2 was abandoned midstream due to the extensive overlap with approach #3. The main objective of this scientific study was to see whether AI could be used in a scientifically appropriate manner to improve the scientific writing process. Indeed, AI reduced the time for writing but had significant inaccuracies. The latter necessitates that AI cannot currently be used alone but could be used with careful oversight by humans to assist in writing scientific review articles., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Artificial intelligence (AI)-driven language models have the potential to serve as an educational tool, facilitate clinical decision-making, and support research and academic writing. The benefits of their use are yet to be evaluated and concerns have been raised regarding the accuracy, transparency, and ethical implications of using this AI technology in academic publishing. At the moment, Chat Generative Pre-trained Transformer (ChatGPT) is one of the most powerful and widely debated AI language models. Here, we discuss its feasibility to answer scientific questions, identify relevant literature, and assist writing in the field of human reproduction. With consideration of the scarcity of data on this topic, we assessed the feasibility of ChatGPT in academic writing, using data from six meta-analyses published in a leading journal of human reproduction. The text generated by ChatGPT was evaluated and compared to the original text by blinded reviewers. While ChatGPT can produce high-quality text and summarize information efficiently, its current ability to interpret data and answer scientific questions is limited, and it cannot be relied upon for a literature search or accurate source citation due to the potential spread of incomplete or false information. We advocate for open discussions within the reproductive medicine research community to explore the advantages and disadvantages of implementing this AI technology. Researchers and reviewers should be informed about AI language models, and we encourage authors to transparently disclose their use., (© The Author(s) 2023. Published by Oxford University Press on behalf of European Society of Human Reproduction and Embryology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Artificial intelligence (AI) tools, although beneficial for data collection and analysis, can also facilitate scientific fraud. AI detectors can help resolve this problem, but their effectiveness depends on their ability to track AI progress. In addition, many methods of evading AI detection exist and their constantly evolving sophistication can make the task more difficult. Thus, from an AI-generated text, we wanted to: (1) evaluate the AI detection sites on a text generated entirely by the AI, (2) test the methods described for evading AI detection, and (3) evaluate the effectiveness of these methods to evade AI detection on the sites tested previously., Hypothesis: Not all AI detection tools are equally effective in detecting AI-generated text and some techniques used to evade АI detection can make an AI-produced text almost undetectable., Materials and Methods: We created a text with ChatGPT-4 (Chat Gеnеrаtivе Prе-trained Transformer) and submitted it to 11 АI detection web tools (Оriginаlity, ZеrоGPT, Writеr, Cоpylеаks, Crоssplag, GPTZеrо, Sapling, Cоntеnt аt scаlе, Cоrrеctоr, Writеfull еt Quill), bеfоrе аnd аftеr applying strategies to minimise AI detection. The strategies used to minimize AI detection were the improvement of command messages in ChatPGT, the introduction of minor grammatical errors such as comma deletion, paraphrasing, and the substitution of Latin letters with similar Cyrillic letters (a and о) which is also a method used elsewhere to evade the detection of plagiarism. We have also tested the effectiveness of these tools in correctly identifying a scientific text written by a human in 1960., Results: From the initial text generated by the AI, 7 of the 11 detectors concluded that the text was mainly written by humans. Subsequently, the introduction of simple modifications, such as the removal of commas or paraphrasing can effectively reduce AI detection and make the text appear human for all detectors. In addition, replacing certain Latin letters with Cyrillic letters can make an AI text completely undetectable. Finally, we observe that in a paradoxical way, certain sites detect a significant proportion of AI in a text written by a human in 1960., Discussion: AI detectors have low efficiency, and simple modifications can allow even the most robust detectors to be easily bypassed. The rapid development of generative AI raises questions about the future of scientific writing but also about the detection of scientific fraud, such as data fabrication., Level of Evidence: III Control case study., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

Introduction: Artificial intelligence has started to become a part of scientific studies and may help researchers with a wide range of tasks. However, no scientific studies have been published on its ussefulness in writing cover letters for scientific articles. This study aimed to determine whether Generative Pre-Trained Transformer (GPT)-4 is as good as humans in writing cover letters for scientific papers., Methods: In this randomised non-inferiority study, we included two parallel arms consisting of cover letters written by humans and by GPT-4. Each arm had 18 cover letters, which were assessed by three different blinded assessors. The assessors completed a questionnaire in which they had to assess the cover letters with respect to impression, readability, criteria satisfaction, and degree of detail. Subsequently, we performed readability tests with Lix score and Flesch Kincaid grade level., Results: No significant or relevant difference was found on any parameter. A total of 61% of the blinded assessors guessed correctly as to whether the cover letter was written by GPT-4 or a human. GPT-4 had a higher score according to our objective readability tests. Nevertheless, it performed better than human writing on readability in the subjective assessments., Conclusion: We found that GPT-4 was non-inferior at writing cover letters compared to humans. This may be used to streamline cover letters for researchers, providing an equal chance to all researchers for advancement to peer-review., Funding: This study received no financial support from external sources., Trial Registration: This study was not registered before the study commenced., (Published under Open Access CC-BY-NC-BD 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/.)

Rapid advancements in artificial intelligence (AI) have revolutionized numerous sectors, including medical research. Among the various AI tools, OpenAI's ChatGPT, a state-of-the-art language model, has demonstrated immense potential in aiding and enhancing research processes. This review explores the application of ChatGPT in medical hospital level research, focusing on its capabilities for academic writing assistance, data analytics, statistics handling, and code generation. Notably, it delves into the model's ability to streamline tasks, support decision making, and improve patient interaction. However, the article also underscores the importance of exercising caution while dealing with sensitive healthcare data and highlights the limitations of ChatGPT, such as its potential for erroneous outputs and biases. Furthermore, the review discusses the ethical considerations that arise with AI use in health care, including data privacy, AI interpretability, and the risk of AI-induced disparities. The article culminates by envisioning the future of AI in medical research, emphasizing the need for robust regulatory frameworks and guidelines that balance the potential of AI with ethical considerations. As AI continues to evolve, it holds promising potential to augment medical research in a manner that is ethical, equitable, and patient-centric., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2023 The Author(s).)

The academic and scientific world in general is increasingly concerned about their inability to determine and ascertain the identity of the writer of a text. More and more often the question arises as to whether a scientific article or work handed in by a student was actually produced by the alleged author of the questioned text. The role of artificial intelligence (AI) is increasingly debated due to its dangers of undeclared use. A current example is undoubtedly the undeclared use of ChatGPT to write a scientific text. The article promotes an AI model-independent redundancy measure to support discrimination between hypotheses on authorship of various multilingual texts written by humans or produced by intelligence media such as ChatGPT. The syntax of texts written by humans tends to differ from that of texts produced by AIs. This difference can be grasped and quantified even with short texts (i.e. 1800 characters). This aspect of length is extremely important, because short texts imply a greater difficulty of analysis to characterize authorship. To meet the efficiency criteria required for the evaluation of forensic evidence, a probabilistic approach is implemented. In particular, to assess the value of the redundancy measure and to offer a consistent classification criterion, a metric called Bayes factor is implemented. The proposed Bayesian probabilistic method represents an original approach in stylometry. Analyses performed over multilingual texts (English and French) covering different scientific and human areas of interest (forensic science and socio-psycho-artistic topics) reveal the feasibility of a successful authorship discrimination with limited misclassification rates. Model performance is satisfactory even with small sample sizes., (© 2023. The Author(s).)

The release of text-generating applications based on interactive Large Language Models (LLMs) in late 2022 triggered an unprecedented and ever-growing interest worldwide. The almost instantaneous success of LLMs stimulated lively discussions in public media and in academic fora alike not only on the value and potentials of such tools in all areas of knowledge and information acquisition and distribution but also on the dangers posed by their uncontrolled and indiscriminate use. This conversation is now particularly active in the higher education sector, where LLMs are seen as a potential threat to academic integrity at all levels, from facilitating cheating by students in assignments to plagiarizing academic writing in the case of researchers and administrators. Within this framework, we are interested in testing the boundaries of the LLM ChatGPT (www.openai.com) in areas of our scientific interest and expertise and in analyzing the results from different perspectives, i.e. of a final year BSc student, of a research scientist, and of a lecturer in higher education. To this end, in this paper, we present and discuss a systematic evaluation on how ChatGPT addresses progressively complex scientific writing tasks and exam-type questions in Carbohydrate Chemistry and Glycobiology. The results of this project allowed us to gain insight on: (i) the strengths and limitations of the ChatGPT model to provide relevant and (most importantly) correct scientific information, (ii) the format(s) and complexity of the query required to obtain the desired output, and (iii) strategies to integrate LLMs in teaching and learning., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The use of artificial intelligence (AI) in medical research is on the rise. This article explores the role of using ChatGPT, a language model developed by OpenAI, in writing medical scientific articles. The material and methods used included a comparative analysis of medical scientific articles produced with and without the use of ChatGPT. The results suggest that the use of ChatGPT can be a useful tool for scientists to increase the production of higher quality medical scientific articles, but it is important to note that AI cannot fully replace human authors. In conclusion, scientists should consider ChatGPT as an additional tool to produce higher quality medical scientific articles more quickly., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

Large language models (LLMs) such as ChatGPT have garnered considerable interest for their potential to aid non-native English-speaking researchers. These models can function as personal, round-the-clock English tutors, akin to how Prometheus in Greek mythology bestowed fire upon humans for their advancement. LLMs can be particularly helpful for non-native researchers in writing the Introduction and Discussion sections of manuscripts, where they often encounter challenges. However, using LLMs to generate text for research manuscripts entails concerns such as hallucination, plagiarism, and privacy issues; to mitigate these risks, authors should verify the accuracy of generated content, employ text similarity detectors, and avoid inputting sensitive information into their prompts. Consequently, it may be more prudent to utilize LLMs for editing and refining text rather than generating large portions of text. Journal policies concerning the use of LLMs vary, but transparency in disclosing artificial intelligence tool usage is emphasized. This paper aims to summarize how LLMs can lower the barrier to academic writing in English, enabling researchers to concentrate on domain-specific research, provided they are used responsibly and cautiously., Competing Interests: Sung Il Hwang, the section editor of the Korean Journal of Radiology, was not involved in the editorial evaluation or decision to publish this article. All authors have declared no conflicts of interest., (Copyright © 2023 The Korean Society of Radiology.)

ChatGPT is a generative artificial intelligence chatbot which may have a role in medicine and science. We investigated if the freely available version of ChatGPT can produce a quality conference abstract using a fictitious but accurately calculated data table as applied by a non-medically trained person. The resulting abstract was well written without obvious errors and followed the abstract instructions. One of the references was fictitious, known as 'hallucination'. ChatGPT or similar programmes, with careful review of the product by authors, may become a valuable scientific writing tool. The scientific and medical use of generative artificial intelligence, however, raises many questions., (© 2023 The Authors. Emergency Medicine Australasia published by John Wiley & Sons Australia, Ltd on behalf of Australasian College for Emergency Medicine.)

Background: With emergence of chatbots to help authors with scientific writings, editors should have tools to identify artificial intelligence-generated texts. GPTZero is among the first websites that has sought media attention claiming to differentiate machine-generated from human-written texts., Methods: Using 20 text pieces generated by ChatGPT in response to arbitrary questions on various topics in medicine and 30 pieces chosen from previously published medical articles, the performance of GPTZero was assessed., Results: GPTZero had a sensitivity of 0.65 (95% confidence interval, 0.41-0.85); specificity, 0.90 (0.73-0.98); accuracy, 0.80 (0.66-0.90); and positive and negative likelihood ratios, 6.5 (2.1-19.9) and 0.4 (0.2-0.7), respectively., Conclusion: GPTZero has a low false-positive (classifying a human-written text as machine-generated) and a high false-negative rate (classifying a machine-generated text as human-written)., Competing Interests: The author has no potential conflicts of interest to disclose., (© 2023 The Korean Academy of Medical Sciences.)

Objective: Scientific writing in English is a daunting task for non-native English speakers. The challenges of writing in a foreign language are evident in the scientific literature where texts by non-native English-speaking scientists tend to be less clear and succinct, contain grammatical errors, and are often rejected by prestigious journals., Methods: We conducted a non-systematic review of the most recent literature using the terms "Artificial Intelligence," "Scientific Writing," and "Non-English Speaking" to create a narrative review., Results: Artificial intelligence can be a solution to improve scientific writing, especially for non-native English-speaking scientists. Artificial intelligence can assist in the search for pertinent scientific papers, generate summaries, and help with the writing of different sections of the manuscript, including the abstract, introduction, methods, results, and discussion. Artificial intelligence-based programs can correct grammatical errors and improve writing style, both of which are particularly helpful for non-native English speakers. Two artificial intelligence programs that can help with the search for pertinent scientific papers on the internet are Elicit and ResearchRabbit. Scispace Copilot can be used to summarize the retrieved reference. The artificial intelligence software programs such as Grammarly and Paperpal can correct grammatical and spelling errors, while ChatGPT can also restructure sentences and paragraphs, reword text, and suggest appropriate words and phrases., Conclusion: Overall, artificial intelligence can be an effective tool to improve the clarity, style, and coherence of scientific writing, helping non-native English-speaking scientists to communicate their research more effectively.

Scientific writing can prove challenging, particularly for those who are non-native English speakers writing in English. Here, we explore the potential of advanced artificial intelligence (AI) tools, guided by principles of second-language acquisition, to help scientists improve their scientific writing skills in numerous contexts., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: In this report, we tested ChatGPT's ability to think abstractly and to integrate information about two seemingly disparate topics by requesting a well-articulated, intellectually stimulating essay in response to a complex and somewhat paradoxical task. Method: We asked ChatGPT to write a satirical essay comparing SpongeBob Squarepants to Shakespeare's Hamlet and examined its ability to create a cohesive essay using abstract thinking. Findings: ChatGPT's comparison of Hamlet and SpongeBob was successful, comprehensive, and convincing, demonstrating the ability to make judgments and to use appropriate metaphors and idioms. Conclusions: Our findings suggest that ChatGPT can respond to complex tasks using abstract thinking.

Much has been written about the concerns surrounding artificial intelligence (AI). This article looks positively at how AI can enhance communication and academic skills, including teaching and research. The article explains what AI, Generative Pre-trained Transformer (GPT), and chat-GPT are and highlights a few AI-based tools that are currently in use to improve communication and academic skills. It also mentions potential AI problems, such as a lack of personalization, societal biases, and privacy concerns. The future lies in the training of hand surgeons to master the skill of precise communication and academia using AI tools.

In the first half of 2023, text-generative artificial intelligence (AI), including ChatGPT from OpenAI, has attracted considerable attention worldwide. In this study, first, we compared Japanese stylometric features of texts generated by ChatGPT, equipped with GPT-3.5 and GPT-4, and those written by humans. In this work, we performed multi-dimensional scaling (MDS) to confirm the distributions of 216 texts of three classes (72 academic papers written by 36 single authors, 72 texts generated by GPT-3.5, and 72 texts generated by GPT-4 on the basis of the titles of the aforementioned papers) focusing on the following stylometric features: (1) bigrams of parts-of-speech, (2) bigram of postpositional particle words, (3) positioning of commas, and (4) rate of function words. MDS revealed distinct distributions at each stylometric feature of GPT (3.5 and 4) and human. Although GPT-4 is more powerful than GPT-3.5 because it has more parameters, both GPT (3.5 and 4) distributions are overlapping. These results indicate that although the number of parameters may increase in the future, GPT-generated texts may not be close to that written by humans in terms of stylometric features. Second, we verified the classification performance of random forest (RF) classifier for two classes (GPT and human) focusing on Japanese stylometric features. This study revealed the high performance of RF in each stylometric feature: The RF classifier focusing on the rate of function words achieved 98.1% accuracy. Furthermore the RF classifier focusing on all stylometric features reached 100% in terms of all performance indexes (accuracy, recall, precision, and F1 score). This study concluded that at this stage we human discriminate ChatGPT from human limited to Japanese language., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Zaitsu, Jin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)