Nav: Home

Aiming for accuracy

September 15, 2020

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, PhD, 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, notes that the sense of urgency to develop a vaccine and devise effective treatments for the coronavirus has led many scientists to bypass the traditional peer review process by publishing "preprints" - preliminary versions of their work - online.

While that enables rapid dissemination of new findings, "The problem comes when claims about certain drugs that have not been experimentally validated appear in the preprint world," Oprea says. Among other things, bad information may lead scientists and clinicians to waste time and money chasing blind leads.

AI and machine learning can harness massive computing power to check many of the claims that are being made in a research paper, the suggest the authors, a group of public and private-sector researchers from the U.S., Sweden, Denmark, Israel, France, the United Kingdom, Hong Kong, Italy and China led by Jeremy Levin, chair of the Biotechnology Innovation Organization, and Alex Zhavoronkov, CEO of InSilico Medicine.

"I think there is tremendous potential there," Oprea says. "I think we are on the cusp of developing tools that will assist with the peer review process."

Although the tools are not fully developed, "We're getting really, really close to enabling automated systems to digest tons of publications and look for discrepancies," he says. "I am not aware of any such system that is currently in place, but we're suggesting with adequate funding this can become available."

Text mining, in which a computer combs through millions of pages of text looking for specified patterns, has already been "tremendously helpful," Oprea says. "We're making progress in that."

Since the COVID epidemic took hold, Oprea himself has used advanced computational methods to help identify existing drugs with potential antiviral activity, culled from a library of thousands of candidates.

"We're not saying we have a cure for peer review deficiency, but we are saying that that a cure is within reach, and we can improve the way the system is currently implemented," he says. "As soon as next year we may be able to process a lot of these data and serve as additional resources to support the peer review process."
-end-


University of New Mexico Health Sciences Center

Related Artificial Intelligence Articles:

Artificial intelligence aids gene activation discovery
Scientists have long known that human genes are activated through instructions delivered by the precise order of our DNA.
Artificial intelligence recognizes deteriorating photoreceptors
A software based on artificial intelligence (AI), which was developed by researchers at the Eye Clinic of the University Hospital Bonn, Stanford University and University of Utah, enables the precise assessment of the progression of geographic atrophy (GA), a disease of the light sensitive retina caused by age-related macular degeneration (AMD).
Classifying galaxies with artificial intelligence
Astronomers have applied artificial intelligence (AI) to ultra-wide field-of-view images of the distant Universe captured by the Subaru Telescope, and have achieved a very high accuracy for finding and classifying spiral galaxies in those images.
Using artificial intelligence to smell the roses
A pair of researchers at the University of California, Riverside, has used machine learning to understand what a chemical smells like -- a research breakthrough with potential applications in the food flavor and fragrance industries.
Artificial intelligence could revolutionize sea ice warnings
Today, large resources are used to provide vessels in the polar seas with warnings about the spread of sea ice.
A hidden history of artificial intelligence in primary care
Artificial intelligence methods are being utilized in radiology, cardiology and other medical specialty fields to quickly and accurately process large quantities of health data to improve the diagnostic and treatment power of health care teams.
Identifying light sources using artificial intelligence
Identifying sources of light plays an important role in the development of many photonic technologies, such as lidar, remote sensing, and microscopy.
Artificial intelligence could serve as backup to radiologists' eyes
Deploying artificial intelligence could help radiologists to more accurately classify lung diseases.
Reducing the carbon footprint of artificial intelligence
MIT system cuts the energy required for training and running neural networks.
Researchers rebuild the bridge between neuroscience and artificial intelligence
In an article in the journal Scientific Reports, researchers reveal that they have successfully rebuilt the bridge between experimental neuroscience and advanced artificial intelligence learning algorithms.
More Artificial Intelligence News and Artificial Intelligence Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Debbie Millman: Designing Our Lives
From prehistoric cave art to today's social media feeds, to design is to be human. This hour, designer Debbie Millman guides us through a world made and remade–and helps us design our own paths.
Now Playing: Science for the People

#574 State of the Heart
This week we focus on heart disease, heart failure, what blood pressure is and why it's bad when it's high. Host Rachelle Saunders talks with physician, clinical researcher, and writer Haider Warraich about his book "State of the Heart: Exploring the History, Science, and Future of Cardiac Disease" and the ails of our hearts.
Now Playing: Radiolab

Insomnia Line
Coronasomnia is a not-so-surprising side-effect of the global pandemic. More and more of us are having trouble falling asleep. We wanted to find a way to get inside that nighttime world, to see why people are awake and what they are thinking about. So what'd Radiolab decide to do?  Open up the phone lines and talk to you. We created an insomnia hotline and on this week's experimental episode, we stayed up all night, taking hundreds of calls, spilling secrets, and at long last, watching the sunrise peek through.   This episode was produced by Lulu Miller with Rachael Cusick, Tracie Hunte, Tobin Low, Sarah Qari, Molly Webster, Pat Walters, Shima Oliaee, and Jonny Moens. Want more Radiolab in your life? Sign up for our newsletter! We share our latest favorites: articles, tv shows, funny Youtube videos, chocolate chip cookie recipes, and more. Support Radiolab by becoming a member today at Radiolab.org/donate.