Science | K Huertas
Joshua Wang and others say the easy-to-use platform may be allowing inexperienced researchers—potentially aided by artificial intelligence (AI)—to churn out unreliable and bias-ridden studies with unrivaled speed. “We’ve seen a lot of these TriNetX studies, and they all seem to have very similar flaws,” says Samy Suissa, a pharmacoepidemiologist at McGill University. “They seem to always find these spectacular effects, remarkable benefits for drugs on all kinds of outcomes.” In 2025, nearly 2700 publications mentioned TriNetX in the title or abstract, up from just 33 only 5 years prior, according to the Dimensions database, which tracks abstracts and citations. Less than halfway through this year, the number already exceeds 2100.