Northwestern University researchers are using artificial intelligence (AI) to speed up the search for COVID-19 treatments and vaccines. The AI-powered tool makes it possible to prioritize resources for the most promising studies—and ignore research that is unlikely to yield benefits.
In the midst of the pandemic, scientific research is being conducted at an unprecedented rate. The Food and Drug Administration and the U.S. Department of Health and Human Services announced plans to accelerate clinical trials, and hundreds of scientists are investigating possible treatments and vaccines.
But the question remains: Which research has the most potential to produce real, much-needed solutions?
The scientific community has been predicting the answer to such questions for decades using the Defense Advanced Research Projects Agency’s Systematizing Confidence in Open Research and Evidence (DARPA SCORE) program. The program relies on scientific experts to review and rate submitted research studies based on how likely they are to be replicable. On average, this process takes about 314 days—a long wait in the midst of global pandemic.
The machine model is just as accurate as the human scoring system at making such predictions, researchers said, and it can scale up to review a larger number of papers in a fraction of the time—minutes instead of months.
“The standard process is too expensive, both financially and in terms of opportunity costs,” said Northwestern’s Brian Uzzi, who led the study. “First, it takes take too long to move on to the second phase of testing and second, when experts are spending their time reviewing other people’s work, it means they are not in the lab conducting their own research.”
With their new AI tool, Uzzi and his team at the Kellogg School of Management bypass the human-scoring method, allowing the research community and policymakers to make faster