AI fuels surge in sloppy biomedical research publications

Researchers have identified a troubling trend in biomedical research where artificial intelligence tools may be fueling an explosion of low-quality papers that make misleading health claims. This development threatens to contaminate scientific literature with methodologically flawed studies that draw inappropriate conclusions from publicly available health data, creating a new challenge for maintaining scientific integrity in an era of accessible AI.

The big picture: Scientists have documented a surge in formulaic research papers that appear to use AI to analyze open health data sets, particularly the National Health and Nutrition Examination Survey (NHANES), often producing statistically unsound correlations between single variables and complex diseases.

• A study published in PLoS Biology examined over 300 NHANES-based papers and found many followed suspiciously similar templates that could have been generated by large language models.
• Publication rates have skyrocketed, with more than 2,200 NHANES-based association studies published in 2024 alone, and over 1,200 already published in the first half of 2025.

Behind the numbers: Researchers identified statistical manipulation resembling a form of academic cheating where authors apparently test numerous variables but only report those showing desired correlations.

• “Imagine you’re trying to pass an exam that has a particular pass rate, and you add as many questions as you want. You see which ones you got right, and you remove the ones that you got wrong. That’s basically what they’re doing,” explained Charlie Harrison, a computational biologist at Aberystwyth University.

The methodology: The research team analyzed 341 studies published across a decade (2014-2024) that used NHANES data, appearing in 147 journals from various publishers including Frontiers Media, Elsevier, and Springer Nature.

• NHANES data is particularly vulnerable to misuse because it’s publicly available and already formatted for easy computational analysis.

Why this matters: The proliferation of methodologically flawed research papers threatens scientific integrity and could lead to misinformation about health conditions, potentially affecting medical decision-making and public trust in science.

• The formulaic nature of these papers suggests AI tools may be enabling rapid production of scientific articles without proper statistical rigor or contextual understanding of complex medical conditions.

Expert assessment: Ioana Alina Cristea, a clinical psychologist and meta-researcher at the University of Padua, confirmed the concerning pattern, stating these papers “seem to be written with a recipe” and emphasized the importance of systematic evaluations to gauge the extent of the problem.