Degen, a postdoctoral researcher at the University of Zurich Center for Reproducible Science and Research Synthesis, found that the citing papers all followed a similar pattern. Like the original, they were analyzing the Global Burden of Disease study, a publicly available dataset compiled by the Institute for Health Metrics and Evaluation at the University of Washington. But they were using the dataset to churn out a seemingly endless supply of predictions: about the future likelihood of stroke among adults over 20 years old, of testicular cancer among young adults, of falls among elderly people in China, of colorectal cancer among people who eat minimal whole grains, of disease X among population Y, and so on.

Searching on GitHub for code that would be used to do this sort of analysis, Degen followed some links and wound up on the Chinese social media site Bilibili, where he discovered a Guangzhou-based company touting tutorials on how to produce publishable research in under two hours using its software tools and AI writing assistance. These studies were not very good. Researchers who analyzed a subset of studies about headaches found they were rife with errors and misrepresentations. But they were also not as flagrantly wrong as AI-generated papers of the recent past, making them more difficult to filter out.

“It’s a huge burden on the peer-review system, which is already at the limit,” Degen said. “There’s just too many papers being published and there’s not enough peer reviewers, and if the LLMs make it so much easier to mass produce papers, then this will reach a breaking point.”

Optimists about generative AI have high hopes for its ability to produce future scientific breakthroughs — accelerating discovery, eliminating most types of cancer — but the technology is currently undermining one of the pillars of scientific research, inundating editors and reviewers with an endless stream of papers. Paradoxically, the better the technology gets at producing competent papers, the worse the crisis becomes.