Artificial-intelligence tool aims to reveal whether research findings are supported or contradicted by subsequent studies.

The number of new papers on the COVID-19 pandemic is doubling every two weeks, and shows no sign of slowing. Many of these papers are published first on preprint servers, which means they are made public before having undergone peer review. This makes it all the harder to judge their merit. Now, one start-up company says that its platform — called Scite.ai — can automatically tell readers whether papers have been supported or contradicted by later academic work.

Unlike conventional citation-metrics tools, Scite.ai tells users how often a paper has been supported or contradicted by the studies that cite it, as well as how many times it has simply been mentioned. The resulting reports display citations in the context in which they are mentioned, allowing users to assess for themselves how the paper is being cited.

So far, Scite.ai has analysed more than 16 million full-text scientific articles from publishers such as BMJ Publishing Group in London and Karger in Basle, Switzerland. But that is just a fraction of the scientific literature. “They’re limited by the literature they can get hold of and the machine-learning algorithms,” notes Jodi Schneider, an information scientist at the University of Illinois at Urbana–Champaign.

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