Chinese software that produces ready-to-publish research papers in under two hours threatens peer-review system

Scientific papers are a researcher's most valuable currency, and a paper published in a respected journal can represent several years of work. After noting a surge in citations of her work, statistician Andrea Riebler discovered a vendor in China selling a tool that enables rapid paper mining of public health data. The new software allows anyone to produce ready-to-publish research papers in under two hours.

Riebler is concerned about the growing trend of buying ready-made research tools online. "We found evidence for decentralized mass production of papers based on secondary analysis of public health data. In their tools they also seem to use our work, which is how we found out about the software and how the research started."

Worrying developments

"In our case, we're talking about software that makes it possible to quickly produce simple, standardized analyses and research papers using public health databases. The analyses may be methodologically sound, but the development is worrying nonetheless," says Riebler, a statistician at NTNU's Department of Mathematical Sciences.

The work cannot be properly checked for quality because the authors do not share their source code.

The papers are based on secondary analyses, which present challenges. The data were originally collected by other researchers, and Riebler questions whether every possible combination of the different types of data available in these databases produces relevant or interesting research.

A soaring number of citations

Riebler herself uses data from large registries to identify patterns and potential trends in disease, child mortality and survival analysis across different geographical regions.

In 2017, she published the paper "Projecting the future burden of cancer: Bayesian age–period–cohort analysis with integrated nested Laplace approximations" together with professor Leonhard Held at the University of Zurich.

Riebler had developed software for calculating age-specific forecasts of disease incidence or mortality based on historical data. The source code was freely available, and she had documented how to use it.

In the years that followed, the paper was cited frequently enough to demonstrate that it had made an impact among other researchers.

But then something happened.

The number of citations suddenly soared, from 70 in 2024 to more than 250 the following year.

From cancer burden to diabetes and stroke

Researchers are usually delighted when their work attracts large numbers of citations. But something didn't seem right.

The new papers followed a pattern. Almost all came from Chinese researchers, and most were based on data from the international Global Burden of Disease (GBD) project.

What was peculiar was that the researchers were using GBD data to produce an apparently endless series of forecasts.

The subjects ranged from global, national and regional stroke risk among adults over 20 to testicular cancer in young adults, falls among older people in China, and colorectal cancer associated with very low whole-grain consumption, among others.

"Many of the papers had the same structure. Their titles were similar, with variations relating to different diseases," says Riebler.

"Some of the graphics were based on the same model we used, and their design was very similar to the one I used in my software package. That's unlikely to be a coincidence."

The trail led to China

Researchers Peter Methys Degen, Riebler and Held followed the trail from the cancer paper, which eventually led them to China.

They have now published a paper entitled "Paper mill or paper mine? A tentative answer to the sharp increase in research papers based on the Global Burden of Disease database" in the Journal of Clinical Epidemiology.

"In one of the more than 700 GBD papers we examined, we found a reference to a commercial software package from China. The package includes instructions for doing exactly what has been done in a great many of the other papers," says Riebler.

Research papers in two hours

In the Guangzhou region of China, a company is selling software, AI-powered writing assistance, and instructions that appear to be based on Riebler's research.

The company advertises that its tools make it possible to write research funding applications in just a few minutes, or produce scientific research suitable for publication in under two hours.

The authors also identified two other software packages serving similar purposes, sold by Chinese companies.

Combined with the AI-powered writing assistance these companies also offer, this raises concerns about how scientific publications are being produced, according to the NTNU professor. "This opens up a new way of mass-producing 'research,'" she says.

Drowning in AI-generated papers

"We're not saying that the results are wrong," Riebler emphasizes. "The question is whether each individual paper is scientifically relevant and useful, particularly given the enormous volume of publications we're now seeing."

Overburdened editors at reputable scientific journals believe that mass-produced and AI-generated research is threatening to overwhelm the peer-review system.

The American technology news website The Verge has covered Riebler and her colleagues' findings.

The article describes how journals are drowning in AI-generated submissions. The problem is both the sheer volume of papers and the fact that it is becoming almost impossible to identify which ones were produced using artificial intelligence.

The peer-review system could collapse

"It's a huge burden on the peer-review system, which is already at the limit. There are just too many papers being published and there are not enough peer reviewers, and if the LLMs make it so much easier to mass-produce papers, then this will reach a breaking point," postdoctoral researcher Degen told The Verge.

The journal Accountability in Research, which focuses on research integrity and promoting ethical research practices, has experienced a 60% increase in submissions in 2026.

According to one of its editors, David Resnik, the journal is being bombarded—ironically—with papers that are probably AI-generated and that deal with research misconduct.

"I'm very worried about this straining, breaking the back of the peer-review system," said Resnik to The Verge.

Difficult to detect

NTNU researcher Riebler is herself a member of the editorial boards of two scientific journals. Her inbox, too, is filling up with submissions.

"Then there's the question of whether it really matters if a paper is written by a language model or a human, as long as the content is correct. I don't know the answer to this.

"The problem is the enormous volume of submissions. We're already struggling to find peer reviewers, and the situation is getting progressively worse," says the statistics professor.

Violating a fundamental principle of research

"It's not just data from the Global Burden of Disease project that are being exploited. The same applies to several other major health databases that were established with good intentions and are free and easy to use. It's frightening that this is happening," she says.

For 99% of the papers Riebler and her colleagues examined, the source code was not provided.

This means the research cannot be reproduced, violating a fundamental scientific principle intended to ensure accountability, transparency and quality.

The researchers are calling for better monitoring of online platforms that offer ready-made research tools.

"Sharing code should be mandatory," says Riebler.