Aug 2026· International Review of Research in Open and Distance Learning· 0 citations
TL;DR
It is concluded that AI should serve as an augmentative rather than substitutive technology in peer review, with robust governance frameworks, transparent disclosure mechanisms, and continuous evaluation of equity implications essential for responsible implementation.
Abstract
The integration of artificial intelligence (AI) into scholarly peer review represents a fundamental transformation of academic publishing’s quality control mechanisms. This report critically examined the ethical considerations, institutional practices, and emerging technologies associated with AI-assisted peer review. Drawing on recent policy documents from major publishing organizations, empirical research on AI implementation, and critical scholarship on algorithmic bias, this analysis revealed significant tensions between efficiency gains and integrity preservation. While AI tools have demonstrated potential for addressing reviewer burnout and publication delays, their deployment raises critical concerns regarding confidentiality breaches, accountability gaps, algorithmic bias, and the erosion of expert judgment. Major organizations (e.g., International Committee of Medical Journal Editors) and leading publishers such as Elsevier and Taylor & Francis have emphasized disclosure where AI is used, human accountability, and strict confidentiality controls—often prohibiting uploading unpublished manuscripts into generative AI tools. However, empirical evidence has suggested nontrivial, and potentially growing, undisclosed large language model (LLM)-assisted text in peer review in some conference contexts. This report concluded that AI should serve as an augmentative rather than substitutive technology in peer review, with robust governance frameworks, transparent disclosure mechanisms, and continuous evaluation of equity implications essential for responsible implementation.
Artificial Intelligence (AI) is increasingly being integrated into medical research and scholarly publishing, supporting activities such as literature searching, data analysis, medical imaging, manuscript preparation, and peer review. Despite these opportunities, AI use introduces concerns related to hallucinations, bias, privacy, confidentiality, copyright, reproducibility, and scientific accountability. Existing guidance provides important principles for responsible AI use, but reporting practices remain variable. This article reviews the TITAN guidelines as a practical framework for improving transparency and responsible reporting of AI use in medical research and scholarly publishing. The TITAN guidelines provide a proportionate and technology neutral approach to AI reporting. It distinguishes minor uses, such as language assistance, from substantive applications involving research design, analysis, interpretation, or scientific content. The framework emphasizes that human researchers retain responsibility for evaluating AI outputs and the integrity of published work. It also provides a basis for authors, reviewers, editors, and publishers to incorporate standardized AI reporting into scholarly workflows. TITAN offers a practical framework for documenting meaningful AI involvement while supporting transparency, reproducibility, and human accountability. Its flexible structure can accommodate emerging AI technologies, including multimodal and agentic systems. Periodic revision and coordinated adoption by medical journals and research communities will be important to maintain its relevance as AI capabilities continue to evolve.
Arzoo Nazir, Shah Zeb· Electronic Journal of Medica...· 0 citations
Generative artificial intelligence (AI) is rapidly transforming how scientific knowledge is produced, reviewed, and disseminated. In response, journals and publishing organizations have begun issuing policies to govern AI use in scholarly publishing. However, it remains unclear whether existing governance frameworks meaningfully address the risks AI introduces across the full publication pipeline. We conducted a narrative review of journal policies, publisher guidance, and recent analyses of AI governance in scientific publishing, complemented by direct examination of submission guidelines from high-impact, open-access, and regional medical journals. Our findings show that while most journals have converged on a narrow legal consensus (prohibiting AI authorship and requiring disclosure), yet governance remains fragmented and incomplete. Policies disproportionately target text generation by authors, while leaving critical domains under-regulated, including AI-assisted data analysis, peer review practices, enforcement mechanisms, and equity implications for researchers and reviewers globally. To synthesize these findings, we introduce the AI governance readiness levels, a five-level framework for assessing how well-equipped journals are to govern AI across the research and publication process. We further describe PRAIDE (Preparation, Representation, Attribution, Integrity checks, Dissemination, and Evaluation) as an illustrative architecture that integrates existing policies, integrity safeguards, and post-publication oversight into a coherent governance model. We argue that effective AI governance in scientific publishing cannot be achieved through static rules or journal-centric control alone. Instead, it requires a shift toward shared, adaptive oversight of the scientific publishing system, recognizing that responsibility for governing AI in science is collective, continuous, and inseparable from the public trust in research.
R. Abulibdeh, J. Arslan, S. Ordóñez et al.· MIT Science Policy Review· 1 citation
Artificial intelligence (AI) is increasingly explored as a tool to support institutional decision-making, including within research ethics committees (RECs). However, empirical evidence remains limited regarding how REC members define the acceptable scope of AI integration in ethics review. This study examines how members of Spanish Research Ethics Committees (CEI/CEIm) conceptualise AI use within institutional oversight. A national cross-sectional survey was distributed to 202 committee members and technical secretariat staff, yielding 63 responses (31.2%). The questionnaire combined closed-ended and open-ended items addressing perceived usefulness, acceptable levels of automation, governance concerns, and implementation barriers. Quantitative data were analysed descriptively with exploratory chi-square tests, and qualitative responses were examined using inductive thematic analysis. Respondents expressed strong interest in AI for administrative and documentary support but consistently rejected fully automated ethical decision-making. A statistically significant association was observed between annual workload and interest in AI implementation (χ
2
= 6.20,
p
= 0.045), with higher-workload committees reporting greater openness. Years of experience and institutional role were not significantly associated with attitudes. Across responses, continuous human oversight, accountability, transparency, and data protection emerged as central conditions for acceptability. The findings indicate that AI integration in research ethics oversight is shaped not only by technological feasibility but by institutional concerns regarding responsibility, legitimacy, and deliberative authority. The study contributes empirical insight into how oversight institutions negotiate the acceptable boundaries of AI-assisted decision support in ethically sensitive governance contexts.
Daniel Wang, Marta Guix Arnau, Cristina Llop Julià et al.· Research Ethics· 0 citations
The rapid adoption of large language models and generative artificial intelligence (AI) is transforming biomedical research and publishing. Although international organizations such as the International Committee of Medical Journal Editors (ICMJE) and the Committee on Publication Ethics (COPE) have established the principle that AI cannot be recognized as an author, and the ICMJE, in its January 2026 revision, has introduced a dedicated section addressing AI use by authors, peer reviewers, and editors, journal-level Instructions for Authors still require further operational detail to apply these principles consistently throughout the publication process. This review critically examines current AI-related policies in the Journal of Korean Medical Science (JKMS) and the Korean Association of Medical Journal Editors (KAMJE) and identifies three major challenges: the practical limitations of the AI non-authorship principle, the inadequacy of current AI disclosure practices, and the emergence of AI-specific conflicts of interest that extends beyond conventional financial disclosures. We argue that medical publishing should move from a restrictive approach toward a framework of structured transparency that systematically documents, evaluates, and verifies AI use. To achieve this goal, we propose a practical governance framework that includes a three-tiered AI disclosure system, strengthened accountability for corresponding authors, expanded institutional conflict-of-interest disclosures, transparent reporting of AI use by peer reviewers, and formal editorial policies governing AI-assisted editorial activities. Future revisions of the KAMJE and JKMS Instructions for Authors should prioritize transparent and accountable AI governance rather than restricting AI use. Adoption of a structured, publication-wide framework encompassing authors, peer reviewers, and editors would strengthen research integrity while supporting the responsible integration of AI into biomedical publishing.
Jong-Min Kim· Journal of Korean medical sc...· 1 citation
It is concluded that originality, authorship, integrity, fairness, and transparency are interdependent concerns rather than separate issues, and that responsible AI use is best understood as a disciplined, disclosed collaboration with a non-author tool.
K. A. Badaru· Interdisciplinary Journal of...· 0 citations
The rapid integration of artificial intelligence (AI) tools into scholarly research and publishing has introduced new challenges to research integrity, manifesting in a sharp rise in AI-related retractions. This conceptual review examines the emerging typologies of unethical AI use in academic publishing, including AI-generated textual content with hallucinated citations, AI-assisted data fabrication, AI-generated or manipulated images and figures, AI-enabled peer-review manipulation, and failures to disclose AI involvement. With 764 AI-related retractions identified and a marked spike in 2023, these developments signal systemic vulnerabilities in scholarly communication systems. The paper explores the critical role of librarians as stewards of trustworthy knowledge, educators in AI literacy, and partners in research integrity governance. Librarians are uniquely positioned to detect AI-related misconduct through metadata expertise, to curate tools that flag problematic content, and to guide researchers on ethical AI use aligned with publisher policies and the Committee on Publication Ethics (COPE) guidelines. The review proposes coordinated policy interventions across publishers, journals, libraries, and institutions, emphasizing mandatory AI disclosure, integration of retraction metadata into discovery systems, and capacity-building programs for information professionals. As AI reshapes the research landscape, proactive engagement by librarians is essential to uphold the core values of accuracy, transparency, and accountability in scholarly communication.
Sanmati Jinendran Jain· IP Indian Journal of Library...· 0 citations
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