Along with running their labs, conducting research, and teaching, most scientists often spend unseen evenings and weekends scrutinizing data and crafting feedback as peer reviewers for scientific journals.

“Everyone would agree that the way science publishing works at the moment is far from ideal,” said Oded Rechavi, a molecular biologist at Tel Aviv University. “The bottleneck is the reviewing of the work or evaluating the science, which is just extremely difficult, and currently is done by good, willing people dedicating their time.”

The peer-review process can often be long and tedious, with publishing a paper taking up to 18 months after the initial submission.1 Partly frustrated with this, last year, Rechavi and his team created QED, an AI assistant for reviewing preprints. This would help researchers gauge the quality of a non-peer reviewed paper at a stage where traditional proxies like journal rank and citation count are not available.

Now, building on this, Rechavi and his team developed a metric called the QED Score to rank the validity and originality of findings in a preprint. Rechavi and his team used QED to derive this score from more than 57,000 bioRxiv preprints through blinded evaluations to identify the top one percent life science preprints published in the last year. Moreover, according to a white paper published this week, QED Scores of preprints offered a reasonable estimate of a paper’s quality, per domain experts.

“A lot of research [is] coming every day, and so we need some proxies for quality,” said Pedro Beltrao, a biologist at ETH Zürich, who was not associated with QED Science, Rechavi’s company that developed QED. “Right now, a lot of people just use the journal in which a paper is published as a proxy, and maybe this [QED Score] could be an interesting proxy,” he added. However, he said that such a reduced metric should not be used for the evaluation of scientific careers.