The combination of open-access datasets, machine learning workflows, increased computing capacity, and generative artificial intelligence has effectively removed many of the rate-limiting steps in manuscript production. This has created an industry of click-data science and a flood of low-quality manuscripts based on large health datasets such as the US National Health and Nutrition Examination Survey, the UK Biobank, and the US FDA Adverse Event Reporting System. These papers often employ statistically appropriate methods and real data, but introduce misleading results and false discoveries to the literature. Here, we offer suggestions for editors on how to identify such manuscripts and reject them at the point of submission, reducing the burden on the publishing process.

big data, false discoveries, generative AI, integrity, paper mills