New AI technologies are helping scientists to sort through the wealth of COVID-19 papers -- hopefully hastening the research process.Credit: Adapted from Getty The COVID-19 literature has grown in much the same way as the disease's transmission: exponentially. 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.

We estimate that there are as many as 500,000 papers relevant to COVID-19 that were published before the outbreak, including papers related to the outbreaks of SARS in 2002 and MERS in 2012. Any one of these might contain the key information that leads to effective treatment or a vaccine for COVID-19. This is why we and our colleagues at Lawrence Berkeley National Lab are using the latest artificial intelligence techniques to build COVIDScholar, a search engine dedicated to COVID-19. COVIDScholar includes tools that pick up subtle clues like similar drugs or research methodologies to recommend relevant research to scientists. AI can't replace scientists, but it can help them gain new insights from more papers than they could read in a lifetime.

A team of materials scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) – scientists who normally spend their time researching things like high-performance materials for thermoelectrics or battery cathodes – have built a text-mining tool in record time to help the global scientific community synthesize the mountain of scientific literature on COVID-19 being generated every day. The tool, live at covidscholar.org, The hope is that the tool could eventually enable "automated science." "On Google and other search engines people search for what they think is relevant," said Berkeley Lab scientist Gerbrand Ceder, one of the project leads. "Our objective is to do information extraction so that people can find nonobvious information and relationships. That's the whole idea of machine learning and natural language processing that will be applied on these datasets."

Berkeley Lab researchers (clockwise from top left) Kristin Persson, John Dagdelen, Gerbrand Ceder, and Amalie Trewartha led development of COVIDScholar, a text-mining tool for COVID-19-related scientific literature. A team of materials scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) – scientists who normally spend their time researching things like high-performance materials for thermoelectrics or battery cathodes – have built a text-mining tool in record time to help the global scientific community synthesize the mountain of scientific literature on COVID-19 being generated every day. The tool, live at covidscholar.org, The hope is that the tool could eventually enable "automated science." "On Google and other search engines people search for what they think is relevant," said Berkeley Lab scientist Gerbrand Ceder, one of the project leads.