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Dramatic increases in redundant publications in the Generative AI era.

Danny Maupin1, Tulsi Suchak1, Adrian Barnett2

  • 1School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK.

BMC Medicine
|December 9, 2025
PubMed
Summary
This summary is machine-generated.

Redundant publications have surged due to Generative AI (GenAI), with AI-generated manuscripts evading current plagiarism checks. This indicates a systemic failure in editorial integrity and requires updated guidelines to protect the scientific record.

Keywords:
COPEGenerative AIIntegrityNHANESPaper millsPlagiarismRedundant publication

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Area of Science:

  • Scientific integrity
  • Bibliometrics
  • Health research ethics

Background:

  • Redundant publication distorts the scientific record and wastes resources.
  • Increased use of large open-science data, like NHANES, raises concerns about AI-facilitated redundant manuscripts.
  • Generative AI (GenAI) may be enabling the creation of formulaic, redundant publications.

Purpose of the Study:

  • To quantify redundant publications from a single large health dataset (NHANES).
  • To investigate if GenAI can produce synthetic manuscripts that evade standard integrity checks.
  • To assess the effectiveness of current plagiarism detection in the GenAI era.

Main Methods:

  • Systematic search for redundant publications using NHANES data (2021-2025).
  • Definition of redundancy: same exposures, outcomes, and national population.
  • GenAI prompted to create synthetic manuscripts designed to evade detection; tested with plagiarism software.

Main Results:

  • Identified 411 redundant publications across 156 exposure-outcome pairings.
  • Example: NHANES data on oxidative balance score and chronic kidney disease published 6 times in 1 year.
  • GenAI-generated manuscripts showed low similarity scores (14-26%), evading typical plagiarism thresholds.

Conclusions:

  • A 17-fold increase in redundant publications (2022-2024) signals a systemic failure in editorial checks.
  • Redundant papers distort research (meta-analyses, scientometrics) and waste peer review resources.
  • Current plagiarism and redundancy checks are inadequate for GenAI; publishers need enhanced cooperation and guidelines.