AI (Artificial Intelligence) has a long history, going back to the 1950s when the computer industry started. It’s interesting to note that much of the innovation came from government programs, not private industry. This was all about how to leverage technologies to fight the Cold War and put a man on the moon.
The impact of these program would certainly be far-reaching. They would lead to the creation of the Internet and the PC revolution.
So fast forward to today: Could the COVID-19 pandemic have a similar impact? Might it be our generation’s Space Race?
I think so. And of course, it’s just not the US this time. This is about a worldwide effort.
The Catalysts
Wide-scale availability of data will be key. The White House Office of Science and Technology has formed the Covid-19 Open Research Dataset, which has over 24,000 papers and is constantly being updated. This includes the support of the National Library of Medicine (NLM), National Institutes of Health (NIH), Microsoft and the Allen Institute for Artificial Intelligence.
“This database helps scientists and doctors create personalized, curated lists of articles that might help them, and allows data scientists to apply text mining to sift through this prohibitive volume of information efficiently with state-of-the-art AI methods,” said Noah Giansiracusa, who is the Assistant Professor at Bentley University.
Yet there needs to be an organized effort to galvanize AI experts to action. The good news is that there are already groups emerging. For example, there is the C3.ai Digital Transformation Institute, which is a new consortium of research universities, C3.ai (a top AI company) and Microsoft. The organization will be focused on using AI to fight pandemics.
There are even competitions being setup to stir innovation. One is Kaggle’s COVID-19 Open Research Dataset Challenge, which is a collaboration with the NIH and White House. This will be about leveraging Kaggle’s 4+ million community of data scientists. The first contest was to help provide better forecasts of the spread of COVID-19 across the world.
Next, the Decentralized Artificial Intelligence Alliance is putting together Covidathon, an AI hackathon to fight the pandemic coordinated by SingularityNET and Ocean Protocol. The organization has more than 50 companies, labs and nonprofits.
And then there is MIT Solve, which is a marketplace for social impact innovation. It has established the Global Health Security & Pandemics Challenge. In fact, a member of this organization, Ada Health, has developed an AI-powered COVID-19 personalized screening test.
Free AI Tools
AI tools and infrastructure services can be costly. This is especially the case for models that target complex areas like medical research.
But AI companies have stepped up—that is, by eliminating their fees:
- NVIDIA is providing a free 90-day license for Parabricks, which allows for using AI for genomics purposes. Consider that the technology can significantly cut down the time for processing. The program also involves free support from Oracle Cloud Infrastructure and Core Scientific (a provider of NVIDIA DGX systems and NetApp cloud-connected storage).
- DataRobot is offering its platform for no charge. This allows for the deployment, monitoring and management of AI models at scale. The technology is also provided to the Kaggle competition.
- Run:AI is offering its software for free to help with building virtualization layers for deep learning models.
- DarwinAI has collaborated with the University of Waterloo’s VIP Lab to develop COVID-Net. This is a convolutional neural network that detects COVID-19 using chest radiography. DarwinAI is also making this technology open source (below you’ll find a visualization of this).
Patient Care
Patient care is an area where AI could be essential. An example of this is Biofourmis. In a two-week period, this startup created a remote monitoring system that has a biosensor for a patient’s arm and an AI application to help with the diagnosis. In other words, this can help reduce infection rates for doctors and medical support personnel. Keep in mind that–in China–about 29% of COVID-19 deaths were healthcare workers.
Another promising innovation to help patients is from Vital. The founders are Aaron Patzer, who is the creator of Mint.com, and Justin Schrager, an ER doc. Their company uses AI and NLP (Natural Language Processing) to manage overloaded hospitals.
Vital is now devoting all its resources to create C19check.com. The app, which was built in a partnership with Emory Department of Emergency Medicine’s Health DesignED Center and the Emory Office of Critical Event Preparedness and Response, provides guidance to the public for self-triage before going to the hospital. So far, it’s been used by 400,000 people.
And here are some other interesting patient care innovations:
AliveCor: The company has launched KardiaMobile 6L, an AI-enabled platform, that allows healthcare professionals to measure QTc (heart rate corrected interval) in COVID-19 patients. The QTc measurement can help detect sudden cardiac arrest. It’s based on the FDA’s recent guidance to allow more availability of non-invasive remote monitoring devices for the pandemic.
CLEW: It has launched the TeleICU. It uses AI to identify respiratory deterioration in advance.
Drug Discovery
While drug discovery has made many advances over the years, the process can still be slow and onerous. But AI can help out.
For example, a startup that is using AI to accelerate drug development is Gero Pte. It has used the technology to better isolate compounds for COVID-19 by testing treatments that are already used in humans.
“Mapping the virus genome has seemed to happen very quickly since the outbreak,” said Vadim Tabakman, who is the Director of technical evangelism at Nintex. “Leveraging that information with Machine Learning to explore different scenarios and learn from those results could be a game changer in finding a set of drugs to fight this type of outbreak. Since the world is more connected than ever, having different researchers, hospitals and countries, providing data into the datasets that get processed, could also speed up the results tremendously.”
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