How we used AI to help in the fight against COVID-19



Centro Medico Santagostino

Centro Medico Santagostino

CHANNEL

Proprietary interface

SECTOR

Healthcare



CHALLENGE

COVID-19 put the scientific community to the test

Covid-19 has been a disruptive event in many areas. It reached the scientific community like a tsunami of research papers, scholarly articles and a massive amount of textual information. There is a growing urgency to process and make sense of this information because of the rapid acceleration in new coronavirus literature, making it difficult for the medical research community to keep up.


In response to this issue, the White House and a coalition of leading research groups have issued the COVID-19 Open Research Dataset Challenge (CORD-19): "a call to action to the world’s artificial intelligence experts to develop text and data mining tools that can help the medical community develop answers to high priority scientific questions".

SOLUTION

Looking for meaningful answers

After months of research we were able to build RECORD, a proprietary AI model that can help researchers to find the right information amidst a sea of data. We partnered with Centro Medico Sant’Agostino, a Milan-based healthcare facility, to validate the AI model and the precision of the answers. The model is currently being used for testing purposes and it could be used in a production environment to empower a Covid-19 virtual assistant capable of answering the most advanced scientific questions about the virus.


The analysis conducted along with the scientific experts shows that RECORD provides remarkable results in terms of consistency and precision of the answers. AI language technologies proved to be exceptionally suited to face this kind of challenge as you can see from the results below. That's why we aim to make RECORD a valid ally for researchers and scientific communities that are studying one of the greatest challenges humanity has ever encountered.


RESULTS

Performances of the model




195k

scholarly articles in the dataset

+80%

of correct answers from topic-specific questions

+62%

of correct answers form medical and scientific experts




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