Google, UC Berkeley developing AI tools to sift through thousands of COVID-19 research articles

With the National Institutes of Health's COVID-19 research publication database nearing 30,000 articles available, University of California Berkeley and Google are just two of numerous organizations developing artificial intelligence tools to help clinicians and researchers sift through the literature, according to Nature.

Coupled with the urgency of the pandemic and the tech advances in natural-language processing, researchers have crafted AI-powered tools that find studies most relevant to what the user is looking for. As of June 11, the NIH's COVID-19 Portfolio website, which tracks papers published about the novel coronavirus and the diseases it causes, lists more than 29,000 articles.

A team at UC Berkeley developed COVIDScholar, which provides a search function that allows users to enter relevant terms relating to the virus and will deliver research articles pertaining to that topic. Google launched the COVID-19 Research Explorer, which allows users to as questions such as "What are the rapid molecular diagnostics for COVID-19?" It then generates a list of research articles, according to the report.

The tech giant was already working on the website as a biomedical-research tool prior to the pandemic, but launched it when the White House released the COVID-19 Open Research Dataset in March. The dataset is open to researchers on a global scale to develop new text and data to mining techniques to navigate the COVID-19 literature.

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