On Artificial Intelligence and the Transformation of Scientific Publishing

A Tool for Reviewers

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d641542e353"> <b>Objective:</b> This study aimed to examine the attitudes and perceptions of Editors-in-Chief (EiCs) of biomedical journals regarding the integration of artificial intelligence chatbots (AICs) into the scholarly publishing process. While AICs offer opportunities to streamline editorial tasks such as plagiarism detection, language editing, and ethics screening, they also introduce ethical, technical, and operational challenges. Understanding EiC perspectives is critical to shaping guidelines, policies, and training programs that align with the evolving role of AICs in scientific publishing. <b>Design:</b> We conducted a cross-sectional survey of EiCs from biomedical journals published by Springer &amp; BMC (part of Springer Nature), Taylor &amp; Francis, Elsevier, Wiley, and SAGE, which are the five largest academic publishers by journal count. Eligible journals were identified through a combination of automated web scraping of publisher webpages and manual verification. A total of 3381 EiCs were invited via email to participate in an anonymous online survey conducted over five weeks in 2024, which included three follow-up reminders. The survey covered familiarity with AICs, current usage, perceived benefits and challenges, and anticipated future roles. Quantitative data were analyzed using descriptive statistics, while qualitative responses underwent thematic content analysis to identify key themes. <b>Results:</b> Of the 3381 EiCs contacted, 510 responded (15.1% response rate), with 505 eligible participants and a completion rate of 87.0%. Most respondents were familiar with AICs (66.7%, 325/487) but had not used them in editorial workflows (83.7%, 401/479). Perceived benefits included enhanced language and grammar support (70.8%, 308/435) and plagiarism screening (67.3%, 294/437). However, respondents expressed concerns about initial setup and training (83.9%, 360/429), ethical risks (80.6%, 345/428), and technical reliability (75.2%, 322/428). While only 49.6% (240/484) of journals reported having formal AIC policies, 89.5% (419/468) of respondents supported training initiatives to promote ethical and effective usage. Despite limited current adoption, 78.9% (370/469) believed AICs will play an important role in the future of scholarly publishing, and 77.2% (363/470) anticipated their significance in advancing scientific research. Themes identified through thematic analysis of open-ended questions include: “no AI in authorship or peer review” referring to the EiC current journal/publisher policy on AIC use, and “ethical, integrity, and privacy concerns” referring to EiC perceptions of challenges with the use of AICs in the scholarly publishing process. <b>Conclusions:</b> Biomedical journal EiCs recognize AICs’ potential to enhance editorial processes but highlight critical barriers, including ethical dilemmas, resource limitations, and insufficient policies and training. Structured interventions, including targeted training programs and robust ethical guidelines, are essential for addressing these challenges and ensuring responsible and effective integration of AICs into publishing workflows.

The rapid integration of generative artificial intelligence (AI) tools like ChatGPT into academic writing has created an ethical grey zone for authors, reviewers, and editors. While these tools can enhance clarity and language, their unchecked use raises serious concerns about authorship, accountability, plagiarism, and data fabrication. Yet, the editorial community remains divided: Should AI-assisted writing be treated as ghost authorship? Can reviewers ethically use AI for peer review summaries? Do journals require disclosure only when AI is used by authors, or also when used during editorial workflows? Amid these unresolved tensions, leading medical journals and editorial bodies such as COPE and WAME are introducing policies that vary widely in clarity and enforcement. Some allow AI for grammar correction; others prohibit its use entirely in data interpretation. However, most journals lack consistent language on how to declare, limit, or audit AI use — leaving authors confused and reviewers unregulated. This short communication explores the gaps in current journal policies on AI use and calls for unified, transparent guidelines that balance innovation with integrity. We advocate for a three-tiered declaration system: (1) AI-assisted writing, (2) AI-generated content, and (3) AI use in peer review and editorial decision-making. As medical education and public health researchers increasingly turn to AI for manuscript preparation, the editorial community must act quickly to avoid reputational risks and uphold trust in the publishing process.

Editorial policies for ethical use of artificial intelligence in rheumatology journals.

Artificial Intelligence in Biomedical Scientific Publishing.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

The discourse surrounding the use of artificial intelligence (AI) in higher education has been dominated by a sense of intrigue, uncertainty, and apprehension. Higher education is faced with the challenge of addressing this emerging technology within their academic settings. In order to tackle the use of AI in the classroom head-on, a teaching team consisting of an instructor and a librarian sought to showcase how AI can be conscientiously and responsibly integrated into the existing curriculum. The teaching team designed an instruction module with two goals in mind: first, to train students on how to use a leading AI large language model generative chatbot, ChatGPT, and second, on how to analyze and interpret the synthetically generated outputs. This paper presents a preliminary analysis of the efficacy and impact of this instructional module. The approach adopted by the teaching team is grounded in the American Association of Colleges and Universities (AAC&U) Valid Assessment of Learning in Undergraduate Education (VALUE) Rubrics for Information Literacy. In particular, students were exposed to the following three dimensions: Accessing the Needed Information, Evaluating Information and its Sources Critically, and Accessing and Using Information Ethically and Legally. In this paper, the term Artificial Intelligence Literacy (AI-L), refers to equipping students with skills necessary to evaluate AI critically and apply these new skills to their work responsibly. Introduction to Engineering Experience is a required course offered every Fall semester to all first-year engineering students at an undergraduate engineering institution. The course is grounded on the approach of Raymond Landis, who coined the term World Class Engineering Student (WCES). The approach focuses on development of soft skills including collaboration, reflection, peer review, and time management; skills which are increasingly recognized as an important part of student development and success in engineering education, and essential in the development of WCES's. In the current Fall 2023 semester, an AI-L module was added, with the belief that AI is an emerging, and essential component of being a WCES. The AI-L module was delivered in two sessions to 128 students. In the first session, students were asked to create their own ChatGPT account, and were instructed on how generative large language model AI systems work. In small groups and as a whole class, students discussed how to create effective prompts, and how ChatGPT responds to prompts. In the second session, students learned how ChatGPT processes information and constructs its outputs. Students also discussed the limitations of AI bots, and learned the importance of thinking critically about AI-generated content. Subsequently, the teaching team equipped students with a rubric based on the VALUES dimensions to evaluate content generated by AI tools. In this paper, a preliminary assessment of the effectiveness of this AI-L instruction module is presented. Initial observations indicate that the module seems to have been effective in training students to recognize and critically evaluate AI-generated content. Students were surprised and intrigued by how much AI is capable of doing, and eager to learn how to be responsible users of such technologies. As a result of this instruction module, this cohort of first year engineering students seemed to possess a more comprehensive understanding of how ChatGPT can both facilitate and potentially hinder their educational and professional development. This empowered them to make informed decisions about the extent and manner in which they wish to engage with AI technology in their current studies and in the future as engineers and as lifelong learners.

Artificial Intelligence (AI) has revolutionized several industries, and scientific publishing is no exception. From peer review automation to the detection of plagiarism and manuscript proofing, AI is revolutionizing research production, dissemination, and evaluation. Although AI brings tremendous potential to automate publishing, it raises significant questions regarding ethics and integrity that must be addressed correctly (1). The most significant impact that AI has made on publishing is speeding up the peer review process. Traditional peer review is laborious and time-consuming, and it tends to result in very lengthy publication cycles. AI tools can assist in pre-screening submissions, finding possible reviewers according to expertise, and even identifying ethics concerns like duplicate publication or image tampering. Some AI tools, like ScholarOne and Editorial Manager, have already started using machine learning algorithms to recommend reviewers and detect probable conflicts of interest, making an efficient and unbiased review process possible (2). Besides peer review, AI has also improved the editorial process by employing language processing models that assist authors in manuscript editing. These include products like Grammarly, Writefull, and Paperpal, which use AI-driven natural language processing (NLP) to correct grammar, simplify the language, and improve readability. This proves helpful in non-native English-speaking academics, who may be unable to present research findings effectively. Also, AI-driven translation software is breaking language barriers, allowing for research dissemination across linguistic groups (3). The detection of plagiarism has also shifted fundamentally with the advent of AI. Conventional software such as Turnitin and iThenticate have come a long way, using deep learning algorithms to identify more evolved instances of academic fraud, including paraphrasing plagiarism and AI-generated content. After the proliferation of generative AI tools such as ChatGPT, the difference between human-written and machine-written content has become more difficult to distinguish, calling for increasingly sophisticated AI-driven authenticity checks (4). But publishing with AI is not without issues. The ethical aspects of AI-generated research content are becoming a problem more and more. The greater the dependence on AI writing aids, the greater the problems concerning authorship, novelty, and intellectual property. The majority of journals now have strict policies for using AI-generated content and being open and accountable in scientific publishing. Second, the risk of bias in AI algorithms is still an issue because AI algorithms learn from what is already in print form, thus potentially continuing existing biases in publishing materials (5). Besides that, AI is transforming the availability of scientific literature. AI-based recommendation platforms such as Semantic Scholar and Scite simplify scientists' ability to locate pertinent literature by analyzing citation patterns and trend research. Open-access journals also leverage AI to increase content published therein and make it more accessible and readable to audiences, thereby democratizing knowledge dissemination. Despite all these advancements, human judgment remains unavoidable in publishing. No matter how much help AI will extend, ethical decisions and contextual appreciation are still impossible without human ability. Symbiosis with AI as a helper, but not a replacement for writers, editors, and referees, is the best means to achieve this (6). Last but not least, AI is undoubtedly revolutionizing the terrain of scientific publishing. Its capacity to simplify workflows, enhance quality, and enable accessibility is charting the future of academic communication. With these opportunities, however, come the ethics and integrity issues that need to be resolved in order to achieve responsible AI deployment. As the world of publishing advances, a delicate balance that harnesses the power of AI while maintaining the integrity of academics will be critical in sustaining trust and credibility within the science literature.

Introduction: This article reports results from a survey of faculty members with editorial responsibilities. The survey explored what publishing services and platform functionalities respondents found most valuable in their work as editors, how satisfied they were with the services provided by commercial publishers, and to what extent they were aware of alternative publishing practices.&amp;nbsp;Method: The authors used data collected from a survey instrument that was distributed to a sample (n = 515) of faculty members with editorial responsibilities at their institution. Results: Collected data suggest that faculty editors value specific publishing services (e.g., coordination of peer review and copyediting) and platform functionality (e.g., submission and peer-review management) more than others, recognize several challenges facing academic publishing in their disciplines (including the transition to open access publishing models), and are mostly aware of common forms of open access research dissemination such as open access journals and institutional repositories. Discussion: The survey results may be helpful to library publishers in making decisions about what publishing services and platform functionalities to prioritize in the development of their publishing programs. In addition to utilizing the survey data to assess the needs of editors, the authors also identified a number of expanded uses of the survey related to marketing and outreach. Conclusion: Insofar as faculty editors are key stakeholders that library publishers seek to build partnerships with, it is important to understand their needs and preferences as editors. This article provides some insight into these questions that may prove helpful to library publishers.

Journal Performance Report for 2007

There is a substantial body of scientific literature on the use of third-party services (TPS) by academics to assist as “publication consultants” in scholarly publishing. TPS provide a wide range of scholarly services to research teams that lack the equipment, skills, motivation, or time to produce a paper without external assistance. While services such as language editing, statistical support, or graphic design are common and often legitimate, some TPS also provide illegitimate services and send unsolicited e-mails (spam) to academics offering these services. Such illegitimate types of TPS have the potential to threaten the integrity of the peer-reviewed scientific literature. In extreme cases, for-profit agencies known as “paper mills” even offer fake scientific publications or authorship slots for sale. The use of such illegitimate services as well as the failure to acknowledge their use is an ethical violation in academic publishing, while the failure to declare support for a TPS can be considered a form of contract fraud. We discuss some literature on TPS, highlight services currently offered by ten of the largest commercial publishers and expect authors to be transparent about the use of these services in their publications. From an ethical/moral (i.e., non-commercial) point of view, it is the responsibility of editors, journals, and publishers, and it should be in their best interest to ensure that illegitimate TPS are identified and prohibited, while publisher-employed TPS should be properly disclosed in their publications.

All conference organisers/editors are required to declare details about their peer review. Therefore, please provide the following information:• Type of peer review: Single-blind Single-blind peer review-reviewers’ identities are kept hidden from authors.• Conference submission management system: Open Conf Peer Review and Conference Management System• Number of submissions received: 78 manuscripts.• Number of submissions sent for review: 76 manuscripts.• Number of submissions accepted: 51 manuscripts.• Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 65%• Average number of reviews per paper: 3 reviewers.• Total number of reviewers involved: 108 reviewers.• Any additional info on review process: Manuscript score with similarity check less than 30% after screening with Turnitin plagiarism software will be sent for a single-blind review process. The manuscripts were evaluated based on the technical criteria, quality criteria, and presentation criteria. The main aspect when assessing the manuscripts is quality and clarity of the content, originality, contribution to the related field, organization and readability as well as the quality of English. The reviewers were asked to fill in the peer review form and review questions from OpenConf Peer Review and Conference Management System. There are six reviewer’s decisions on the review process including (i) must accept, (ii) clear accept, (iii) marginal tend to accept, (iv) marginal tend to reject, (v) probable reject, and (vi) reject with specifying their confidence (low, moderate, high) regarding qualifications to review the allocated paper. After editors received the review manuscripts, the authors were asked to revise and resubmit their manuscript according to the reviewer’s comments.• Contact person for queries: Dr. Nurbaiti WahidSchool of Electrical Engineering, College of Engineering,Universiti Teknologi MARA, Terengganu, MALAYSIAnurbaiti@uitm.edu.my

New Scholars Writing for Publication.

This paper examines how generative artificial intelligence (AI) is reshaping scientific publishing through its growing role in authoring, reviewing, and editorial workflows. It maps the current landscape of generative AI adoption, highlighting how large-scale language models and image-based systems assist researchers and editors in writing, translation, visualisation, and content management. The analysis identifies both opportunities, such as efficiency, inclusivity, and innovation, and challenges, including accuracy, accountability, bias, and uneven policy implementation. Ethical implications are addressed through international and national frameworks that emphasise transparency, human oversight, and provenance verification. By synthesising recent literature and publisher policies, this paper argues for the institutionalization of ethics-in-practice as a foundation for trustworthy AI integration in scholarly communication.

Artificial intelligence (AI) has been increasingly integrated into medical publishing, hopefully improving efficiency and accuracy, but serious concerns persist regarding ethical implications, authorship attribution, and content reliability. We aimed at understanding the perspectives of editors of medical journals on AI. A structured online questionnaire was developed and distributed to editors-in-chief of medical journals worldwide. The survey comprised 27 concise questions exploring demographics, journal practices, and perspectives on AI in editorial workflows. Quantitative data were analyzed using descriptive statistics to summarize usage patterns, perceived benefits, risks, and future expectations. A total of 59 editors-in-chief completed the survey (response rate: 19%), with replies suggesting substantial variability in beliefs and attitudes toward AI for publication in medical journals. Artificial intelligence tools were already in use by 49% of journals, mainly for plagiarism detection (76%) and data verification (35%). Only 9% of responders reported that journals used AI for both scientific and linguistic review. Time savings (79%) and cost reduction (43%) were the most commonly cited benefits, and concerns included potential bias (71%) and lack of accountability (60%). Overall, 81% of responders anticipated a major role for AI in publishing within 10 years. Exploratory analyses suggested several potential associations between replies and respondent or journal features, requiring further validation in future surveys. In conclusion, this survey on attitudes toward AI in publication in medical journals suggests that editors-in-chief are cautiously adopting AI in their editorial workflow, supporting its operational use while explicitly calling for clear guidance to address ethical and regulatory concerns.