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Using AI tools to help fight against COVID-19

Mounir Jamil

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Inside Look into AI Tools against COVID-19

Prior to the world being even aware of the threat posed by the COVID-19 virus, artificial intelligence (AI) systems had already successfully detected an outbreak of an unknown type of pneumonia in China. As the coronavirus spread, AI tools have been deployed to support the efforts made by the medical community, policy makers and society at large to manage the stages of the crisis and its aftermath.

Using AI to understand and treat COVID-19
AI tools can help the medical community and policymakers understand the COVID-19 virus further and accelerate research in treatments via rapidly analyzing big volumes of research data. Furthermore, data mining tools and AI text can reveal the virus’ history, diagnostics, transmission, management measures and can give lessons from past epidemics.

Deep learning models have the ability to predict old and new drugs and treatments that might be successful in treating coronavirus. Several institutions are utilizing AI to identify treatments and develop prototype vaccines.

Dedicated platforms allow the sharing and consolidation of multidisciplinary expertise on AI. The US government has started a dialogue with international government science leaders that include utilizing AI in accelerating the analysis of coronavirus literature that is made available using the Kaggle Platform.

Furthermore, computing power for AI is also being made available by technology companies like Microsoft, Google, Amazon and individuals donating computer processing power, alongside public-private efforts like the COVID-19 High Performance Computing Consortium and AI for Health.

Using AI in the diagnosis, detection, and prevention of COVID-19
AI can also be employed to help detect, diagnose and prevent the spread of the virus. Algorithms that identify patterns and anomalies are already working to detect and predict the spread of COVID-19, while image recognition systems are speeding up medical diagnosis. For example:

AI against COVID-19 can also be used to diagnose, detect, and prevent the spread of the virus. Algorithms can identify anomalies and patterns and they are already working on detecting and predicting the spread of the virus, all while image recognition systems are speeding up medical diagnosis.

Early warning systems powered by AI, can help detect epidemiological patterns by mining mainstream news, and online content with other information channels in multiple languages to provide early warnings. These early warnings complement syndromic surveillance and other healthcare networks and data flows.

AI tools also identify virus transmission chains and monitor broader economic impacts. AI technologies have demonstrated their potential in inferring epidemiological data more rapidly than other traditional reporting methods.

Rapid diagnosis is another of the AI tools against COVID-19, and it is the key to limiting contagion and understanding how the virus spreads.

Furthermore semi-autonomous drones and robots are being used to respond to the immediate needs of hospitals such as delivering medicine, equipment and food, sterilizing, cleaning and aiding medical staff.

Junior social media strategist with a degree in business. Passionate about technology, film, music and video games.

MedTech

Impacts of the pandemic on SMEs: First in, first out

Adnan Kayyali

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Impacts of the pandemic on SMEs First in, first out

The pandemic sent shockwaves across the world with many SMEs bearing the brunt of the crisis due to the reduction in global demand for goods and services.  

The worst effect of the pandemic on SMEs were the mass layoffs seen throughout all industries, although disproportionately. Disposable income that could have circulated in the economy became scarce, leaving many SMEs susceptible to permanent closure as people spend their money with greater caution. This was only a few weeks into the crisis, and prior to any government aid.

Business owners had very different predictions about the duration of the pandemic, leading them to make varied decisions on whether or not to keep their employees, cut their losses, or whether to save up or spend their stimulus checks. Many business owners were paying from their own pocket to stay afloat, and could not last more than a few weeks or few months, with layoffs.

A survey of more than 5,800 small businesses between March 28 and April 4, 2020 was conducted to determine how adaptable businesses were to the sudden change of the market and social landscape, and the impacts of the pandemic on SMEs.

According to the survey, 92% of SMEs changed at least one thing in their business model to adapt to COVID-19, most using some form of digital technology to bypass, adapt, or improve many traditional – potentially risky – ways of doing business.

Noting that some companies selected more than one option, the changes were listed as follows:

  • 58% of businesses said that they had adopted a new online delivery channel
  • 40% created new virtual services
  • 36% listed the use of a new offline delivery channel, such as Uber Eats.
  • 31% had released a new product.
  • 19% new customers

Consequently, the survey also listed the 5 most commonly mentioned challenges that these businesses have experienced:

  • 22% lack of employee skills
  • 16% lack of adequate funds
  • 14% setting up new online delivery channels
  • 9% developing new products.
  • 8% faced challenges adapting to the new health and safety standards
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MedTech

Top 3 digital health technologies post- pandemic

Mounir Jamil

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Top 3 digital health technologies post- pandemic

It’s certain that the current pandemic will eventually come to an end. However, some of the digital health technologies we’ve adopted along the way have proven to be indispensable, and some technologies may not be so prominent after the crisis.

Here are top 3 digital health technologies that are likely to stick around post- pandemic.

1. Disinfectant robots

Ultraviolet (UV) lights, more specifically UV-C is a well-established digital disinfecting method that is commonly used in the healthcare world. It works by altering the virus’ genetic material, that way UV lights make sure that virus doesn’t replicate. However, if exposed to human skin, it can lead to sunburns, irritations, and in the worst case, skin cancer.

All that aside, the benefits of UV-C in effectively disinfecting hospitals is seriously attracting robotics engineers and healthcare workers alike. Companies such as YouiBot are reimagining and redesigning their current robots into UV disinfectant ones. Danish company UVD Robots has shipped hundreds of their existing disinfectant robots around the world during the pandemic.

In addition to saving valuable time and decreasing the spread of COVID-19 in hospitals, these robots will also prevent hospitals from getting infections.

2. AI for predicting future pandemics

In an ideal tech world, AI can predict a viral outbreak weeks, if not months in advance. Unfortunately, we don’t live in an ideal tech world, but the good news is that we can work towards forecasting such a system with the help of current technologies.

AI company BlueDot has issued early first warnings, after scrutinizing massive data sets from news, airlines, and animal disease outbreaks. Their algorithim managed to detect a certain trend which Epidemiologists later analyzed further to confirm an outbreak.

But BlueDot is the exception and not the rule, so we must reverse the situation in order to better handle the next public health crisis. Given the massive predictive power that AI brings to the healthcare sector, the proper authorities should utilize its full potential and help in making it more commonplace in hospital settings.

3. Remote care via smartphones 

It’s a sad reality that the pandemic kick-started telemedicine for mainstream adoption. Before the crisis, only 1 in 10 US patients used telemedicine services, the number has now increased up to 158% in the same country.

With lockdowns enforced globally, people are utilizing the power of their smartphones for their mental and physical wellbeing. These new digital health technologies greatly reduce the risk of cross infection all while offering patients quality care from the comfort of their own homes

These solutions greatly reduce the risk of cross-infection while offering patients quality care from the comfort of their homes. What’s more, they prove that face-to-face doctor-patient visits are unnecessary. A Global Markets Insights report from April this year, projects that the telemedicine market value will reach $175.5 billion by 2026, indicating the need for remote care in the coming years.

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MedTech

The glaring problem with COVID-19 vaccine deployment

Adnan Kayyali

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The glaring problem with COVID-19 vaccine deployment

As most of us already know, creating a vaccine is only half the challenge of beating the pandemic. Getting 7 billion people vaccinated is a colossal undertaking, the scale of which has never been seen in history. How will the world manage and sustain COVID-19 vaccine deployment, and ensure equitable access to everyone?

It is not an easy task, and many people in positions of responsibility may have to make some difficult decisions. In short, we will not have enough vaccines for everyone by the end of this year, even if a particular vaccine candidate is deemed adequate.

In a document by the CDC published as a rough skeletal guideline, four categories of people were prioritized with newly produced or procured vaccine doses. This is to strategically use the scarce resources available to minimize the loss of life and maximize equity.

The document classified four categories of people that would receive the vaccine at different times according to a number of factors:

Category 1:

– High risk healthcare workers. First responders

– High risk older adults in congregate or crowded settings

Category 2:

Workers in critical industries and those living in an environment of high risk such as prison.

Category 3:

Young adults and children, and workers of essential industries that were not included in phase 2.

Category 4:

Everyone else.

In an Audio Interview “Guidelines for Covid-19 Vaccine Deployment”, Eric J. Rubin, M.D., Ph.D. concurred. “We do this in medicine all the time”, he said “in that we plan to treat everybody but those who get sicker are the ones who need the treatment first, while we are scaling up or making any assessment of deploying a treatment”.

This task becomes more difficult in areas were the data on who needs what is scarce. Numerous collectives and institutions however are finding ways to guide their communities using localized data tracking, remote monitoring and some forms of contact tracing. They will be able to identify where and how many people require vaccination, how many vaccines are available for the taking, when more is coming, etc.

Having a clear picture is essential for any major endeavor to succeed, and a type of “communal immunity” can be achieved, to break the back of community transmission” as Rubin put it.

The issue of Covid-19 Vaccine Deployment isn’t when the vaccine is coming, but “who gets it first”. The answer given by the CDC seems to be a good one, from the perspective of the scientist, who have accepted the reality that vaccine equity is no easy task, and hard decisions must be made.

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