Journals infiltrated with ‘copycat’ papers that can be written by AI

An analysis of a literature database finds that text-generating artificial intelligence (AI) tools — including ChatGPT and Gemini — can be used to rewrite scientific papers and produce ‘copycat’ versions that are then passed off as new research.

In a preprint posted on medRxiv on 12 September¹, researchers identified more than 400 such papers published in 112 journals over the past 4.5 years, and demonstrated that AI-generated biomedicine studies could evade publishers’ anti-plagiarism checks.

The study’s authors warn that individuals and paper mills — companies that produce fake papers to order and sell authorships — might be exploiting publicly available health data sets and using large language models (LLMs) to mass-produce low-quality papers that lack scientific value.

“If left unaddressed, this AI-based approach can be applied to all sorts of open-access databases, generating far more papers than anyone can imagine,” says Csaba Szabó, a pharmacologist at the University of Fribourg in Switzerland, who was not involved in the work. “This could open up Pandora’s box [and] the literature may be flooded with synthetic papers.”

Redundant research

To investigate, researchers screened association studies — those that statistically link a variable to a health outcome — that were based on data from the US National Health and Nutrition Examination Survey (NHANES), a huge open repository of data on the health, diet and lifestyles of thousands of people.

They focused their search on studies they defined as ‘redundant’, meaning that the work tested the association between the same variable and health outcome as other research did, but analysed a subtly different subset of the actual data — including results from different survey years, for example, or participants of a different age or sex.

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Their search of the PubMed index of biomedical literature revealed 411 redundant studies published between January 2021 and July 2025. Most were simple ‘repeat’ cases, involving two publications that were almost identical. But three associations had a particularly high number of duplicate studies — six papers apiece — some of which were published in the same year.

This “shouldn’t be happening, and it doesn’t help the health of the scientific literature”, says co-author Matt Spick, a biomedical scientist at the University of Surrey in Guildford, UK.

Most publishers have checks in place to prevent researchers submitting the same research to multiple journals, but Spick and his colleagues suspect that AI tools are being used to evade these.

Avoiding detection

To test whether AI can help to produce multiple papers from the same data set, the researchers used OpenAI’s chatbot ChatGPT and Google’s Gemini to rewrite three of the most heavily redundant articles identified by their analysis (each reported a particular association that had already been published five or six times). The researchers prompted the LLMs to use the information in each paper, and NHANES data, to produce a new manuscript that could avoid plagiarism detectors.

“We were shocked that it worked straight away,” says Spick. “They weren’t perfect, and the LLMs did create some errors. It took two hours of cleaning-up work for each manuscript.”

Rise of ChatGPT and other tools raises major questions for research

When analysed with a plagiarism-detection tool used by many publishers, the AI-generated manuscripts did not score at a level that would be considered problematic by editors. This shows that LLMs “can produce something that is derivative of everything that’s gone before and doesn’t include anything new. But it’ll still pass plagiarism checks”, says Spick. This, in turn, makes it more difficult to distinguish between researchers who are producing genuine research using public-health data sets such as NHANES, and others who are deliberately creating redundant papers using LLMs, the authors note.