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Professor of Medicine and Pharmaceutical SciencesChief, Division of Translational InformaticsThe University of New Mexico. Credit: The University of New Mexico
As the COVID-19 pandemic has swept the world, researchers have published hundreds of papers each week reporting their findings—many of which have not undergone a thorough peer review process to gauge their reliability.
In some cases, poorly validated research has massively influenced public policy, as when a French team reported COVID patients were cured by a combination of hydroxychloroquine and azithromycin. The claim was widely publicized, and soon U.S. patients were prescribed these drugs under an emergency use authorization. Further research involving larger numbers of patients has cast serious doubts on these claims, however.
With so much COVID-related information being released each week, how can researchers, clinicians and policymakers keep up?
In a commentary published this week in Nature Biotechnology, University of New Mexico scientist Tudor Oprea, MD, Ph.D., and his colleagues, many of whom work at artificial intelligence (AI) companies, make the case that AI and machine learning have the potential to help researchers separate the wheat from the chaff.
Oprea, professor of Medicine and Pharmaceutical Sciences and chief of the UNM Division of Translational Informatics, …
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