The COVID-19 literature has grown in much the same way as the disease’s transmission: exponentially. The NIH’s COVID-19 Portfolio , a website that tracks papers related to the SARS-CoV-2 coronavirus and the disease it causes, lists more than 28,000 articles — far too many for any researcher to read (See ‘Explosive Growth’; code and data at https://github.com/jperkel/covidlit ). But a fast-growing set of artificial-intelligence (AI) tools might help researchers and clinicians to quickly sift through the literature. Driven by a combination of factors — including the availability of a large collection of relevant papers, advances in natural-language processing (NLP) technology and the urgency of the pandemic itself — these tools use AI to find the studies that are most relevant to the user, and in some cases to extract specific findings from the results. Beyond the current pandemic, such tools could help to bridge fields by making it easier to identify solutions from other disciplines, says Amalie Trewartha, one of the team leads for the literature-search tool COVIDScholar , at the Lawrence Berkeley National Laboratory in Berkeley, California. The tools are still in development, and their utility is largely unproven. They can’t be used to make clinical or research decisions. Even using AI, “a vaccine is not going to emerge full-blown”, says Oren Etzioni, chief executive of the Allen Institute for AI (AI2) in Seattle. But developers hope the new technology will help researchers to focus their efforts. “Augmented intelligence is the best summary of AI,” Etzioni says. 链接地址： https://www.nature.com/articles/d41586-020-01733-7