Image: iStock.com/Udom Pinyo
Millions of people across the world have already started the process of receiving a COVID-19 vaccines. More than half of all adults in the U.S. have gotten at least one dose of a COVID-19 vaccine while state and local officials seek to get even more people vaccinated as quickly as possible. Some health experts have said artificial intelligence will be integral not just in managing the process of creating boosters for the variants to COVID-19 but also for the distribution of the vaccine.
David Smith, associate VP of virtual medicine at UMass Memorial Health Care, explained that the difference between predictive modeling and AI, or machine learning, is that predictive models depend on past data to foretell future events.
AI, on the other hand, not only uses historical data, it makes assumptions about the data without applying a defined set of rules, Smith said.
“This allows the software to learn and adapt to information patterns in more real time. The AI utility for vaccine distribution could be applied in a variety of ways from understanding which populations to target to curve the pandemic sooner, adjusting supply chain and distribution logistics to ensure the most people get vaccinated in the least amount of time, to tracking adverse reactions and side effects,” he noted.
SEE: AI in healthcare: An insider’s guide (free PDF) (TechRepublic Premium)
Matthew Putman, an expert in artificial intelligence and CEO of Nanotronics, has been working with one of the top vaccine developers and said AI was helping teams manage the deluge of data that comes with a project like this.
While the number of vaccinated people in the country continues to rise by the millions each day, there is already some concern about how the vaccines will hold up against the multitude of variants.
The biggest challenge right now and the biggest opportunity for changing the way that therapeutics are both developed and deployed, Putman explained, is being able to handle new types of variants.
“In the case of mRNA vaccines, being able to actually do reprogramming as fast as possible in a way that is as coordinated as possible. The things that we have realized in many parts of our lives now is that as good as humans are at exploring things and being creative, being able to deal with enough data and to be able to make intelligent choices about that data is something that actually artificial intelligence agents can do at a pace that is required to keep up with this,” Putman said.
“So it means a lot of multivariate correlations to different parts of the process. It means being able to detect potential intrusion and it’s a way that we can avoid these lengthy phase three trials. Everything that’s going on right now is so incredibly urgent.”
Putman added that an AI system would help with building actionable data sets that allow doctors to examine root causes or things that researchers don’t have time to spend on.
When researchers are dealing with things like lipid nanoparticles and the tasks of imaging and classifying different features and trends that are on a scale, it can be difficult for humans to manage. AI is now being used for analyzing these images in real time and has helped researchers try to pick out genetic mutations and variations, according to Putman.
“People are more open to AI than ever, and this emergency has brought a focus on things that probably would have been on the backburner. But AI is starting to be used for classification and to understand what genomic features and what type of nano compounding has been going on,” Putman added.
“AI has been used for the development of components and much more. It’s been crucial to the process and will be crucial to an alteration to the vaccine, which is looking like it will have to be done at some point. The way I look at contemporary AI systems, it’s taking into account what move is being made next. This is Alpha Go for drug discovery. A virus will mutate in different ways and now a response to that can be developed in new ways.”
Putman went on to compare the situation to the yearly creation of a new flu vaccine, noting that once you’ve grown a lot of biological specimens, it’s a slow tedious process to change for new mutations.
“Using mRNA, it’s not, and using AI for being able to see what changes are going on from everywhere from the sequence to the quality inspection is a big deal,” Putman said.
When asked about the production of boosters for variants, Putman said adjusting a process usually takes years just for a normal product, and if you’re doing something as new as what is going on with the vaccine and you’re dealing with the entirety of the supply chain, the process has to be adjusted as fast as the science does.
“We have the science now. We’ve shown that these types of vaccines can be developed. Now, making sure that your production process stays the same, even if you’ve adjusted something else, is something that if it’s put in place, the process will adjust,” Putman said.
“If an AI system worked for this or an intelligent factory system is put into place, then the process can adjust as quickly as the R&D can. Without AI, it would be very difficult.”
Cheryl Rodenfels, a healthcare strategist at Nutanix, echoed those remarks, explaining that AI can be an incredibly useful tool when it comes to vaccine distribution.
Organizations that utilize workflow improvement processes can harness AI tools to ensure that the processes are being followed as designed and that any missing elements are identified, Rodenfels said, adding that this process plays into vaccine tracking measures specifically, as AI will track vaccine handling, storage and administration.
“Relying on the technology to manage distribution data eliminates human error, and ensures that healthcare organizations are accurately tracking the vast amounts of data associated with the vaccine rollout,” Rodenfels said.
“However, the biggest problem with using AI to assist with vaccine rollout is that each manufacturer has its own process and procedure for the handling, storage, tracking, training and administration of the vaccine. This is then complicated by the amount of manufacturers in the market. Another issue is that hospital pharmacies and labs don’t have a lot of extra space to stage and set up the doses. In order to insert effective AI, a hospital would need to ensure a process architect and a data scientist work collaboratively.”
These issues are compounded by the fact that there is no baseline for how these things are supposed to work, she noted. The measurements, analytics and information will be developed on the fly, and because it is unknown how many vaccines each organization will be required or allowed to have, it is difficult to predict the capacity or amount of data that will be produced.
The advantage to using AI in vaccine rollout is that it will set us up for success during round two of vaccine dosing. It will also positively impact future vaccine dissemination by creating a blueprint for the next mass inoculation need, both Rodenfels and Putman said.
Walter McAdams, SQA Group senior vice president of solutions delivery, said that AI will be useful in analyzing how the virus is mutating over time, how variations could affect current vaccine make-ups, and how to use that information to accelerate the development of virus countermeasures.
Researchers, he said, can leverage data about how COVID-19 has mutated and vaccine effectiveness to continuously refine the vaccine sequence and, in some cases, get ahead of COVID-19 and prepare new vaccines before additional strains fully develop.
