How Machine Learning Is Helping Us Get Through One of the Largest Pandemics in History
Although AI and machine learning are similar, they aren’t considered the exact same because machine learning is only a subset of AI. Both of these concepts were developed in the 1950s however, machine learning was ahead of its time.
This is due to a man by the name of Arthur Samuel, in the late 1950’s he came to the realization that everything we need to teach computers how to do, can be self-taught to themselves. When he realized that there can be a way for the computer to teach itself, he also came to the realization that this could result in far more efficient when it came to certain tasks.
But now you might be wondering, how do we even do this?
This is where the development of neural networks enters the pictures. Neural networks serve as a computer that has specifically been designed to operate by sorting, identifying, and classifying data and information in the same way as a human.
For example, it’s been taught to recognize elements within images and classify them based on those elements. It essentially works based on a system of probability, depending on the information that is fed to it, it returns a degree of certainty.
There are endless possibilities that come with machine learning, and if you would like to learn more about it, click here. However, this article is going to focus primarily on the impact machine learning has on the healthcare and medical industry.
“Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed” — Arthur Samuel
AI in the Medical Industry
Each day huge amounts of resources, time, and wealth are wasted in the healthcare systems, not to mention the thousands of people who pass away from simple healthcare errors such as misdiagnosis, delayed treatment plans, or remission rates which could’ve been avoided if the issue was caught on time.
As the world continues to advance, we need to start using these advancements to our advantage for the world to be able to improve and evolve.
This is precisely why numerous healthcare systems around the world are experimenting and working alongside technological innovators and data scientists to discover new, efficient, and innovate ways to diagnose diseases earlier, inform patients and the public on proper health and safety guidelines as well as analyzing past and present data trends and patterns to accurately and rapidly predict trends.
Some popular examples of machine learning merged with healthcare include AI-assisted robotic surgery, paid clinical judgment/diagnosis, and image analysis. A report taken by McKinsey predicts that machine learning and big data could potentially save $100 billion annually due to more accurate and efficient clinical trials, tools, and research.
These new tools and equipment could result in inefficiencies that could drastically aid doctors, regulators, and consumers make important decisions. Furthermore, according to a study done by HIMSS, 77% of healthcare professionals claimed that they believed AI and ML could be best used to support clinical decision making such as the diagnosis process.
Machine Learning and COVID-19
Now more than ever, rapid efficiency and solutions are exactly what the world needs to help cure COVID-19. And for this, every small step that could possibly help towards that is being taken, which is precisely why without any hesitation, healthcare systems have incorporated AI and machine learning hoping it will play a role in increasing our chances of identifying patterns, analyzing trends in COVID-19 which result in faster research and treatment.
Also, healthcare professionals are using it to understand the basics of how the virus transmits and spreads. Machine learning is being widely used for various purposes during this pandemic however, there are some extremely prominent and useful methods being used, such as healthcare institutions commonly using machine learning technologies to screen people for COVID-19.
One of the biggest protocols that should be strictly followed during COVID-19 is social distancing and maintaining a minimum of 6 feet distance.
Normally, to screen or test a patient for something physicians would have to come closer than 6ft however, with the machine learning advancements that we have access to, business and start-ups such Clevy.io have created chatbots that can make it easier and safer for people to gain accurate facts about COVID-19 such as how, where, and why you should get tested all while maintaining social distancing. This chatbot will ask you questions that can help accurately determine COVID-19 risks as well.
Using Machine Learning to Learn How the Virus Spreads
In the initial stages of this unprecedented virus, many things were extremely uncertain to researchers as well as doctors. One of the most important things that we needed to know, we didn’t. And this was how COVID-19 is spreading and how it initially began spreading.
Luckily for us, we had machine learning tools that were especially advantageous because researchers were able to analyze large sets of data to find trends with which they were able to forecast the transmission and spread. Predicting the spread was extremely important because if we knew how the virus spread, we could also find out how to stop the virus from continuing to spread.
Numerous researchers at the Zuckerberg Biohub were able to build a machine learning model that estimated the amount of COVID-19 cases that went undetected, this was useful because it provided the public with a more accurate number of current cases.
Additionally, as they partnered with the AWS diagnostic development initiative they managed not only to discover the number of transmissions that went uncounted but also, to identify how the virus is mutating and spreading through populations.
Furthermore, startups such as ClosedLoop are using their compressive knowledge in healthcare data to create a COVID-19 vulnerability scale, ‘The C-19 Index’, which is used to identify the people that are at most risk from the severe complications that come with treating COVID-19.
The index is also being used by healthcare systems and management to not only identify the high-risk individuals but specifically talking with them about the importance of taking proper precautions to keep themselves safe.
They talk to them about social distancing, handwashing constantly, and they even offer to deliver essential supplies such as toilet paper, food, and water straight to their homes to keep them as safe as possible.
Machine Learning Helping Speed up Treatment and Research
As we progressed further into the virus, we gained more insight and knowledge and, before we knew it we had too much information to keep track of. With this issue in mind, AWS launched a website, CORD-19 Search, which is powered by machine learning intelligence.
The purpose of this website was to make it easier and more efficient for researchers to find the COVID-19 documents they needed. CORD-19 Search currently has possession of 128,000 research papers and extra materials.
Machine learning allows the application to rapidly find specific information from the unstructured text which has proven to quicken the pace of discovery.
Furthermore, in terms of speeding up treatment, engineers from UC San Diego Health have created a machine learning application that detects pneumonia, which has been proven to be associated with severe cases of COVID-19. The advantage of diagnosing this condition earlier is that it allows doctors to treat the patient with the correct level of treatment and care.
Machine learning has almost always proven to be extremely beneficial to us in the past and hasn’t let us down now, with COVID-19 either. As millions more are becoming infected and thousands more are dying each day, tools and technological advancements such as machine learning and AI are the keys to learning more about this mysterious and unknown disease efficiently and efficiently.
Now more than ever, if everyone uses their knowledge together around the world to create and innovate novel solutions, fighting COVID-19 can become one step easier.
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Edited by: Aanya Khan, Director of Research Bloggers
