AI Tools Drive Surge in Papers Analyzing Public Health Datasets

A 2017 paper that assessed the accuracy of a particular type of statistical analysis on epidemiological data received a few dozen citations over the years before experiencing a sudden increase last summer. Peter Degen, a postdoctoral researcher at the University of Zurich Center for Reproducible Science and Research Synthesis, was asked by his postdoctoral supervisor to investigate why the paper was suddenly being cited every few days, hundreds of times.

Degen found that the citing papers all analyzed the Global Burden of Disease study compiled by the Institute for Health Metrics and Evaluation at the University of Washington.

The papers churned out predictions on topics ranging from stroke likelihood among adults over 20 to falls among elderly people in China. Searching on GitHub for related code, Degen discovered a Guangzhou-based company on Bilibili touting tutorials on producing publishable research in under two hours using its software tools and AI writing assistance.

Researchers who analyzed a subset of studies about headaches found they were rife with errors and misrepresentations.

The papers were not as obviously flawed as earlier AI-generated work, making them harder to filter out. “It’s a huge burden on the peer-review system, which is already at the limit,” Degen said. ” Matt Spick, a lecturer in health and biomedical data analytics at the University of Surrey and an associate editor at Scientific Reports, received three strikingly similar papers analyzing the US National Health and Nutrition Examination Survey (NHANES).

There was a sudden explosion in papers citing NHANES that followed a similar formula purporting to discover associations such as eating walnuts and cognitive function or drinking skim milk and depression. “If you’ve got enough computing power, you go through and you measure every single pairwise association, and eventually you find some that haven’t been written on before and you just publish,” Spick said.

Last year, several journals began restricting submissions of papers analyzing public datasets citing a flood of redundant research.

Carnegie Mellon researchers tested several agentic tools and found that they sometimes invented data or used misleading techniques, but the final papers looked polished. OpenAI announced an AI paper writing assistant called Prism earlier this year. Kevin Weil, OpenAI’s then-vice president for science, predicted that 2026 will be for AI and science what 2025 was for AI and software engineering.

Spick and colleagues gave Prism data from an already published paper on ripening times of eggplants and peppers. Prism analyzed it, proposed a new statistical method, and wrote an entire paper with charts and correct citations in 25 minutes and 50 seconds. ’” Spick recalled.

Marit Moe-Pryce is the managing editor of the international relations journal Security Dialogue. Submissions to Security Dialogue are up 100 percent over where they were a year before. One paper made it past at least 10 editors and two rounds of peer review at Security Dialogue before a fake citation was noticed.

The journal Accountability in Research has seen a 60 percent surge in submissions this year, according to David Resnik, an associate editor at Accountability in Research. A survey conducted by the publishing company Frontiers last year found that more than half of researchers have used AI assistance in their peer review.

The number of scientific papers published has grown exponentially in recent years while the number of PhDs who might review them has not, according to Quantitative Science Studies.

A study published in Nature this year found that scientists who adopted AI published three times more papers and received nearly five times more citations than those who didn’t. 37 years earlier than those who did not use AI. In 2022, the scientific organization STM launched an initiative called Integrity Hub to contend with paper mills.

Joris van Rossum is the program director of the Integrity Hub project. Reese Richardson is a postdoctoral fellow at Northwestern University who studies mass-produced papers. The Verge reported that AI agents now threaten to overwhelm the human systems that create and organize knowledge, with research funders, conference organizers, journal editors and peer reviewers all struggling to sort through material that appears good enough at first glance.