Generative Artificial Intelligence in Scientific Publishing: Ethical Governance, Challenges, and Responsibilities
DOI:
https://doi.org/10.20883/medical.e1571Keywords:
scholarly publishing, research integrity, authorship, ethical guidelines, peer review, artificial intelligenceAbstract
Background. The rapid adoption of artificial intelligence (AI) in scholarly publishing is reshaping manuscript preparation, peer review, and editorial workflows. While AI-based tools offer opportunities to enhance efficiency, accessibility, and consistency in scientific communication, their use raises substantial ethical, epistemic, and governance-related concerns, particularly regarding research integrity, transparency, authorship, and accountability.
Material and methods. This narrative review synthesises current evidence on the application of AI in scientific publishing. The analysis integrates empirical studies, policy documents, and position statements from leading international organisations and journals, including COPE, ICMJE, JAMA, WAME, EASE, and GAMER. AI applications were examined across key stages of the publication process: manuscript drafting and language enhancement, figure and reference generation, editorial screening, reviewer selection, and peer review.
Results. AI tools demonstrably enhance linguistic quality, streamline editorial processes, and promote global inclusivity by reducing language barriers. However, recurrent risks were identified, including hallucinated content and references, algorithmic bias, lack of transparency (“black box” decision-making), confidentiality breaches, and misuse in peer review. Current policies consistently prohibit AI authorship and emphasise mandatory disclosure, yet substantial heterogeneity persists in permitted uses, reporting standards, and enforcement mechanisms across journals and publishers.
Conclusions. AI is becoming an integral component of scholarly publishing, offering meaningful benefits alongside significant risks. Responsible integration requires harmonised guidelines, transparent disclosure, rigorous human verification, and sustained editorial oversight. Preserving human judgment and accountability is crucial to ensuring research integrity and maintaining trust in the scientific record as AI technologies continue to evolve.
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