The scientific community worldwide has mobilized with unprecedented speed to tackle the COVID-19 pandemic, and the emerging research output is staggering. Every day, hundreds of scientific papers about COVID-19 come out, in both traditional journals and non-peer-reviewed preprints. There’s already far more than any human could possibly keep up with, and more research is constantly emerging.
And it’s not just new research. 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.
Traditional methods of searching through the research literature just don’t cut it anymore. 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.
Why it matters
When it comes to finding effective treatments for COVID-19, time is of the essence. Scientists spend 23% of their time searching for and reading papers. Every second our search tools can save them is more time to spend making discoveries in the lab and analyzing data.
AI can do more than just save scientists time. Our group’s previous work showed that AI can capture latent scientific knowledge from text, making connections that humans missed. There, we showed that AI