Artificial Intelligence COVID-19

How Artificial Intelligence can Help in Fighting COVID-19

How Artificial Intelligence can Help in Fighting COVID-19
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The recent pandemic caused by COVID-19 has spread across 180 countries, claiming the lives of more than 83,000, and also triggered a near-global lockdown. And at a time, the best solution to prevent the further spreading of the virus is to enhance personal hygiene and practice social distancing.

Meanwhile, politicians, scientists, and researchers are working together to discover systematic approaches to battle the virus and care for patients. And they’re getting lots of needed help from artificial intelligence.
The Artificial Intelligence (AI) tool has proven to correctly predict which patients that have been recently infected with the COVID-19 virus would have severe respiratory disease.

The improvements in AI applications like natural language processing, speech recognition, data analytics, machine learning, deep learning, and others like chatbots and facial recognition have not only been used for diagnosis but also contact tracking and vaccine development. Without a doubt, AI has helped control the effects of COVID-19 pandemic and restrain its worst effects.

How To Use AI To Find Drugs That Target The Virus

Many research projects are using AI to identify drugs that were developed to fight other diseases but could now be repurposed to battle coronavirus. By studying the molecular setup of the already existing drugs with AI, companies want to identify which ones might impede COVID-19 works. A London-based drug-discovery company, BenevolentAI, started shifting its attention towards the coronavirus problem in January ending. The company’s AI-powered knowledge graph can sort out large volumes of scientific literature and biomedical research to discover links between the genetic and biological properties of diseases and the composition and action of drugs.

Can AI bring Researchers Closures to a Cure

One of the greatest things artificial intelligence can do currently is assist researchers search through the data to discover potential treatments.

The COVID-19 Open Research Dataset (CORD-19), an initiative developing on Seattle’s Allen Institute for Artificial Intelligence (AI2) Semantic Scholar project, uses natural language processing to analyze tens of thousands of scientific research papers at an unusual pace.

Some literature-based discovery has great potential to inform vaccine and treatment development, a vital next step in the COVID-19 pandemic. The Semantic Scholar, the team behind the CORD-19 dataset at AI2, was built on the hypothesis that cures for several ills live buried in the scientific literature.

The White House announced the initiative with a coalition that includes the Chan Zuckerberg Initiative, Georgetown University’s Center for Security and Emerging Technology, Microsoft Research, the National Library of Medicine, and Kaggle, the machine learning, and data science community belonged to Google.
Within four days of the dataset’s release on March 16 this year, it got more than 594,000 views and 183 analyses.

How Artificial Intelligence Is Helping In Fighting COVID-19

Identifying vulnerable patients with AI

The study has shown the best indicators of future severity and discovered that they were not as anticipated.
Corresponding author Megan Coffee, clinical assistant professor in the Division of Infectious Disease & Immunology at NYU Grossman School of Medicine, stated: “While work remains to verify our model further, it holds promise as another tool to predict the patients that are most likely to be infected by the virus, but solely in support of physicians’ hard-won clinical experience in treating viral infections.”

Co-author Anasse Bari, Ph.D., a clinical assistant professor in Computer Science at the Courant Institute, stated that: The main goal was to develop and deploy a decision-support tool using AI capabilities, mostly predictive analytics, to signify future clinical coronavirus severity. We expect that the device, when fully developed, will be beneficial to physicians as they determine which moderately ill patients need beds. Those who are safe go home, with hospital resources stretched thin.

Unexpected Predictors

For the study, demographic, laboratory, and radiological discoveries were collected from 53 patients as they all tested positive in January 2020 for COVID-19 at the two Chinese hospitals. In a minority of patients, intense symptoms developed within a week, including pneumonia.

The researchers wanted to discover whether AI techniques could correctly predict which patients with the virus would go on to have Acute Respiratory Distress Syndrome or ARDS. The fluid accumulates in the lungs that can be fatal in the elderly.

To do this, they built computer models that make decisions based on the data fed into them, with programs getting “smarter” the more data they consider. Mainly, the present study used decision trees that trace a series of decisions between options, and that model the potential consequences of choices at every step in a pathway.

The AI tool discovered that changes in three features, levels of the liver enzyme alanine aminotransferase (ALT), reported myalgia, and hemoglobin levels were most precisely predictive of subsequent, severe disease. Along with other factors, the team reported being able to predict the risk of ARDS with up to 80% correctness.
Medicine and public policies

In the circumstances of the pandemic, AI is being applied and delivering results in three areas which include: in virus research and the development of drugs and vaccines; in the management of services and resources at healthcare centers; and in the analysis of data to support public policy decisions aimed at managing the crisis, like the confinement approach.

AI Acts as Disease Surveillance

Infectious disease such as COVID-19 requires surveillance. Human activity, particularly migration- has been responsible for the spread of the virus around the world. Canada-based BlueDot has influenced machine learning and natural language processing to track, recognize, and report the virus’s spread faster than the World Health Organization and the US Centers for Disease Control and Prevention (CDC).

This technology may be used to predict zoonotic infection risk to humans regarding variables like climate change and human activity. The combined analysis of personal, clinical, travel, and social data, family history, and behavioral habits gathered from sources like social media would allow more correct and precise predictions of individual risk profiles and healthcare results.

While concerns may occur about the potential violation of individuals’ civil liberties, policy regulations that other AI applications have faced will ensure that this technology is used responsibly.

Virtual Healthcare Assistants (Chatbots)

The number of COVID-19 cases has revealed that healthcare systems and response measures can be overpowered. Canada-based Stallion.AI has influenced its natural language processing abilities to develop a multi-lingual virtual healthcare agent.

That can answer questions related to COVID-19, offering reliable information and accurate guidelines, recommend protection measures, check and monitor symptoms, and advise people whether they need hospital screening or self-isolation at their homes.

AI Helps in Facial Recognition

For a while now, the thermal cameras have been used for detecting people with fever. The disadvantage to the technology is the requirement for a human operator. Currently, nevertheless, cameras having AI-based multisensory technology have been used in airports, hospitals, nursing homes, etc. The technology detects people with fever and tracks their movements automatically, recognize their faces, and detect whether the person is wearing a face mask or not.

Intelligent Drones & Robots

The public deployment of drones and robots has been progressive due to the strict social distancing measures needed to prevent the spread of the virus. To ensure compliance, some drones are used to track the people not using facemasks in public, while the rest are used to spread information to bigger audiences and disinfect public spaces.

MicroMultiCopter, a Shenzhen-based technology company, has assisted in reducing the virus transmission risk involved with city-wide transport of medical samples and quarantine materials through the implementation of their drones. A patient care, without risk to healthcare workers, has benefited as robots are used for the delivery of food and medication.

The function of room cleaning and disinfection of isolation wards has to been occupied by robots. Catering-industry centered Pudu Technology have enlarged their reach to the healthcare industry by deploying their robots in over 40 hospitals for these purposes.

Verifying the Information

The uncertainty of the pandemic has inevitably led to the propagation of myths on social media platforms. While no quantitative assessment has been done to examine how much misinformation is already out there, it is undoubtedly a reasonably large figure. Technology giants like Google and Facebook struggle to fight the waves of conspiracy theories, phishing, misinformation, and malware.

A search for coronavirus/COVID-19 gives an alert sign along with links to verified sources of information. However, YouTube directly links users to the WHO and comparable, credible organizations for information. Videos that misinform are searched for and taken down immediately; They are uploaded.

Learning for the future from COVID-19

Two of the critical questions surrounding COVID are if and when will things go back to normal and whether we should be ready for subsequent or new waves of coronavirus infections. Though no one has the final answer to these questions, thanks to data analysis, we can now accurately understand what and how it happened—actionable knowledge to control similar crises in the future.

Conclusion

The hope is that the AI can assist radiologists to more quickly and correctly differentiate between COVID-19 infections and other forms of diseases (specifically relevant since cases of flu are widespread still this time of year). And more significantly, decrease the responsibilities for radiologists but allowing other front-line health workers with little expertise to make diagnosis better.

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