For example, AI techniques can help with continuous estimation, optimising the routing of delivery traffic to avoid congestion and other disruptions. One European trucking company lowered their fuel costs by 15 per cent by using sensors tracking vehicle performance and driver behaviour. AI technology coached drivers in real-time, telling them when to speed up or slow down, which in turn increased fuel efficiency, reduced delivery times and brought down maintenance costs.

As AI continues to advance the transportation sector, it has become apparent that it is essential for developing effective vaccine delivery. It can ascertain the best delivery route when vaccines are being carried by planes and trucks, forecasting not only the weather but also using historical data to predict land or air traffic to estimate how long it will arrive in specific locations. This is critical when it comes to overcoming the logistical difficulties of COVID-19 vaccine distribution. 

There have already been millions of people across the world who have started getting their COVID-19 vaccines. In the UK, over 35 million people have had their first dose, and more than half of all adults in the U.S. have gotten at least one dose. When it comes to wide scale vaccine distribution, AI-augmented simulations can also factor in inputs like mobility data, hospital implementation rates, and current case levels. 

Most importantly, cold chain storage is required to preserve the effectiveness of the vaccine. Moderna’s vaccine needs a storage temperature of minus 4 degrees Fahrenheit, while the Pfizer/BioNTech vaccine must be kept at a temperature of about -70C to be effective. Maintaining these temperatures is crucial but not simple, and a 2019 study estimated that 25% of vaccines are damaged by their time of arrival at the final destination. 

To tackle this challenge, Pfizer developed a special transport box around the size of a suitcase, filled with dry ice and installed with GPS trackers. Each reusable box can keep up to 5,000 doses of the vaccine at the right temperature for ten days, as long as it remains unopened. Keeping the container sealed is a crucial element of preserving the vaccine: like any other perishable, they spoil if they get too warm or too cold and have to be thrown away.

The only way to ensure the quality of the vaccine is to stay within the range of the desired temperature when it is delivered. Skymind, the world’s first dedicated AI ecosystem builder, has developed Ultra Low Power AIoT Technology that allows the temperature of the vaccine to be monitored throughout the delivery journey, using Bluetooth intelligent mesh technology. 

While Bluetooth technology has existed since the late 1980s, using it as a mesh has only been possible since 2017. Our own proprietary intelligent mesh technology allows the communication between the devices to be more efficient, which means there’s less network congestion and power savings of up to 98%. As a result, the device lasts longer during the transport journey.

In the case of vaccine deliveries, a small card size device that can last more than half a year, is placed into the box alongside the vaccine and tracks the temperature, moisture, and checkpoint location throughout the journey. The device can share the temperature and communicate it to any laptop or mobile phone, as long as it has Bluetooth. With this technology, the box does not need to be opened by a person at any point until the vaccination is ready to be delivered. 

There are plenty of challenges when it comes to the COVID-19 vaccine delivery but they have provided an unprecedented demand on the solutions that are possible with AI. Ultimately, billions of people worldwide will require at least one dose, making transportation and logistics more critical than ever. Using AI solutions to develop and strengthen the cold chain for vaccine distribution will guarantee that vaccines are not lost and help us overcome the pandemic quicker than we otherwise could.

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Find out more about Digital Transformation Week North America, taking place on November 9-10 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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CDEI advises the government on the responsible use of AI and data-driven technologies. Between June and December 2020, the advisory body polled over 12,000 people to gauge sentiment around how such technologies are being used.

Edwina Dunn, Deputy Chair for the CDEI, said:

“Data-driven technologies including AI have great potential for our economy and society. We need to ensure that the right governance regime is in place if we are to unlock the opportunities that these technologies present.

The CDEI will be playing its part to ensure that the UK is developing governance approaches that the public can have confidence in.”

Close to three quarters (72%) of respondents expressed confidence in digital technology having the potential to help tackle the pandemic—a belief shared across all demographics.

A majority (~69%) also support, in principle, the use of technologies such as wearables to assist with social distancing in the workplace.

Wearables haven’t yet been used to help counter the spread of coronavirus. The most widely deployed technology is the contact-tracing app, but its effectiveness has often come into question.

Many people feel data-driven technologies are not being used to their full potential. Under half (42%) believe digital technology is improving the situation in the UK. Seven percent even think current technologies are making the situation worse.

The scepticism expressed about the use of digital technologies in tackling the pandemic is less about the technology itself – with just 17 percent of respondents expressing that view – and more a lack of faith in whether it will be used by people and organisations properly (39%).

John Whittingdale, Minister of State for Media and Data at the Department for Digital, Culture, Media and Sport, commented:

“We are determined to build back better and capitalise on all we have learnt from the pandemic, which has forced us to share data quickly, efficiently and responsibly for the public good. This research confirms that public trust in how we govern data is essential. 

Through our National Data Strategy, we have committed to unlocking the huge potential of data to tackle some of society’s greatest challenges, while maintaining our high standards of data protection and governance.”

When controlling for all other variables, the CDEI found that “trust that the right rules and regulations are in place” is the single biggest predictor of whether someone will support the use of digital technology.

Among the key ways to help improve public trust is by increasing transparency and accountability. Less than half (45%) of respondents know where to raise concerns if they feel digital technology is causing harm.

CDEI’s research highlighted that people, on the whole, believe data-driven technologies can help tackle the pandemic. However, work needs to be done to improve trust in how such technologies are deployed and managed.

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While the UK’s vaccine rollout is among the fastest in the world – certainly in comparison to its European peers – a series of late lockdowns and initial lack of PPE equipment has put the health service under immense pressure and left the country with one of the highest COVID-19 deaths rates per capita.

The latest lockdown was imposed after ministers were warned the NHS would collapse within a few weeks, leaving Britons unable to access care for any reason. Paramedics have already reported patients dying in ambulances queued outside of hospitals and research from King’s College London suggests around half of ICU staff are suffering from PTSD and problem drinking due to the scenes they’ve witnessed and pressure they’re under.

NHSX, the digital innovation arm of the health service, is now providing access to the National COVID-19 Chest Imaging Database (NCCID) to hospitals and universities across the country to help speed up the diagnosis of the virus and help to reduce the pressure on the health service. 

Clinicians at Addenbrooke’s Hospital in Cambridge are developing an algorithm based on the NCCID images to help inform a more accurate diagnosis of patients when they present to hospital with potential COVID-19 symptoms and have not yet had a confirmed test.

Matt Hancock, Secretary of State for Health and Social Care, said:

“The use of artificial intelligence is already beginning to transform patient care by making the NHS a more predictive, preventive, and personalised health and care service.

It is vital we always search for new ways to improve care, especially as we fight the pandemic with the recovery beyond. This excellent work is testament to how technology can help to save lives in the UK.”

The ability of clinicians to identify the virus in its earliest stages greatly increases the chance that interventions will prevent a progression to severe complications which require longer stays, extra resources, cause more suffering, and will inevitably lead to further deaths.

Carola-Bibiane Schönlieb, Professor of Applied Mathematics and head of the Cambridge Image Analysis group at the University of Cambridge, explained:

“The NCCID has been invaluable in accelerating our research and provided us with a diverse, well-curated, dataset of UK patients to use in our algorithm development.

The ability to access the data for 18 different trusts centrally has increased our efficiency and ensures we can focus most of our time on designing and implementing the algorithms for use in the clinic for the benefit of patients.

By understanding in the early stages of disease, whether a patient is likely to deteriorate, we can intervene earlier to change the course of their disease and potentially save lives as a result.”

Beyond COVID-19, the AI tools being developed are expected to help spot signs of other conditions. A ‘national AI imaging platform’ is being explored to help spot things such as cancers and heart disease in their earliest and most treatable forms.

A £140 million AI award has been established this year in collaboration with the Accelerated Access Collaborative (AAC) and National Institute for Health Research (NIHR) to speed up the deployment of AI technologies to benefit patients and staff across health and care.

Dominic Cushnan, Head of AI Imaging at NHSX, commented: “The industrial scale collaboration of the NHS, research and innovators on this project alone has demonstrated the huge potential and benefits of technology in transforming care.”

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AI is helping with COVID-19 research around the world and much of it is being powered by NVIDIA GPUs. It’s a daunting task, new drugs often cost over $2.5 billion in research and development — doubling every nine years — and 90 percent of efforts fail.

Nvidia wants to help speed up discoveries of vital medicines while reducing costs

“COVID-19 hits home this urgency [for new tools],” Huang says.

Huang announced NVIDIA Clara Discovery—a suite of tools for assisting scientists in discovering lifesaving new drugs.

NVIDIA Clara combines imaging, radiology, and genomics to help develop healthcare AI applications. Pre-trained AI models and application-specific frameworks help researchers to find targets, build compounds, and develop responses.

Dr Hal Barron, Chief Scientific Officer and President of R&D at GSK, commented:

“AI and machine learning are like a new microscope that will help scientists to see things that they couldn’t see otherwise.

NVIDIA’s investment in computing, combined with the power of deep learning, will enable solutions to some of the life sciences industry’s greatest challenges and help us continue to deliver transformational medicines and vaccines to patients.

Together with GSK’s new AI lab in London, I am delighted that these advanced technologies will now be available to help the UK’s outstanding scientists.”

Researchers can now use biomedical-specific language models for their work, thanks to a breakthrough in natural language processing. This means researchers can organise and activate large datasets, research literature, and sort through papers or patents on existing treatments and other vital real-world data.

“Where there are popular industry tools, our computer scientists accelerate them,” Huang said. “Where no tools exist, we develop them—like NVIDIA Parabricks, Clara Imaging, BioMegatron, BioBERT, NVIDIA RAPIDS.”

We’re all hoping COVID-19 research – using such powerful new tools available to scientists – can lead to a vaccine within a year or two, when they have often taken a decade or longer to create.

“The use of big data, supercomputing, and artificial intelligence has the potential to transform research and development; from target identification through clinical research and all the way to the launch of new medicines,” commented James Weatherall, Ph.D., Head of Data Science and AI at AstraZeneca.

During his keynote, Huang provided more details about NVIDIA’s effort to build the UK’s fastest supercomputer – which will be used to further healthcare research – the Cambridge-1.

NVIDIA has established partnerships with companies leading the fight against COVID-19 and other viruses including AstraZeneca, GSK, King’s College London, the Guy’s and St Thomas’ NHS Foundation Trust, and startup Oxford Nanopore. These partners can harness Cambridge-1 for their vital research.

“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Huang. “The Cambridge-1 supercomputer will serve as a hub of innovation for the UK and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”

And, for organisations wanting to set up their own AI supercomputers, NVIDIA has announced DGX SuperPODs as the world’s first turnkey AI infrastructure. The solution was developed from years of research for NVIDIA’s own work in healthcare, automotive, healthcare, conversational AI, recommender systems, data science and computer graphics.

While Huang has a nice kitchen, I’m sure he’d like to be back on the big stage for his GTC 2021 keynote. We’d certainly all love COVID-19 to be well and truly in the rear-view mirror.

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Facebook has been its usual blessing and a curse during the pandemic. On the one hand, it’s helped people to stay connected and organise community efforts. On the other, it’s allowed dangerous misinformation to spread like wildfire that’s led to the increase in anti-vaccine and anti-mask movements.

The social media giant is hoping that AI can help to swing the balance more towards Facebook having an overall benefit within our communities.

If a person has posted asking for help because they’re unable to leave the house, Facebook’s AI may automatically match that person with someone local who has recently said they’re willing to get things like groceries or prescriptions for people.

In a blog post, Facebook explains how it built its matching algorithm:

We built and deployed this matching algorithm using XLM-R, our open-source, cross-lingual understanding model that extends our work on XLM and RoBERTa, to produce a relevance score that ranks how closely a request for help matches the current offers for help in that community.

The system then integrates the posts’ ranking score into a set of models trained on PyText, our open-source framework for natural language processing.

It’s a great idea which could go a long way to making a real positive impact on people in difficult times. Hopefully, we’ll see more of such efforts from Facebook to improve our communities.

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Scientists around the world are doing incredible work to increase our understanding of COVID-19. Thanks to their findings, existing medications have been discovered to increase the likelihood of surviving the virus.

Unfortunately, there are still fatalities. People with weakened immune systems or underlying conditions are most at risk, but it’s a dangerous myth that the young and otherwise healthy can’t die from this virus.

According to a paper published in science journal Nature, around 6.5 percent of COVID-19 cases have a “worrying trend of sudden progression to critical illness”. Of those cases, there’s a mortality rate of 49 percent.

In the aforementioned paper, the researchers wrote: “Since early intervention is associated with improved prognosis, the ability to identify patients that are most at risk of developing severe disease upon admission will ensure that these patients receive appropriate care as soon as possible.”

While most countries appear to be reaching the end of the first wave of COVID-19, the possibility of a second threatens. Many experts forecast another wave will hit during the winter months; when hospitals already struggle from seasonal viruses.

One of the biggest challenges with COVID-19 is triaging patients to decide who are most at risk and require more resources allocated to their care. During the peak of the outbreak in Italy, doctors reported reaching a point of having to make heartbreaking decisions over whether it was a waste of limited resources even trying to save someone.

A team led by China’s senior medical advisor on COVID-19, Zhong Nanshan, was established in February. The team consisted of researchers from Tencent AI Lab in addition to Chinese public health scientists.

Nanshan’s team set out to build a deep learning-based system which can predict whether a patient is likely to become a critical case. Such information would be invaluable to ensuring the patient gets early intervention to improve their chances of surviving the virus in addition to supporting medical staff with their triaging decisions.

The deep learning model was trained on data from 1590 patients from 575 medical centers across China, with further validation from 1393 patients.

Tencent has made the COVID-19 tool for predicting critical COVID-19 cases available online here (Please note the small print which currently says “this tool is for research purpose and not approved for clinical use.”)

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Companies around the world are having to look at new ways of safely continuing business as we adapt to the “new normal” of life with the coronavirus.

Amazon has used its AI expertise to create what it calls the Distance Assistant. Using a time-of-flight sensor, often found in modern smartphones, the AI measures the distance between employees.

The AI is used to differentiate people from their background and what it sees is displayed on a 50-inch screen for workers to quickly see whether they’re adhering to keeping a safe distance.

Augmented reality is used to overlay either a green or red circle underneath each employee. As you can probably guess – a green circle means that the employee is a safe distance from others, while a red circle indicates that person needs to give others some personal space.

The whole solution is run locally and does not require access to the cloud to function. Amazon says it’s only deployed Distance Assistant in a handful of facilities so far but plans to roll out “hundreds” more “over the next few weeks.”

While the solution appears rather draconian, it’s a clever – and arguably necessary – way of helping to keep people safe until a vaccine for the virus is hopefully found. However, it will strengthen concerns that the coronavirus will be used to normalise increased surveillance and erode privacy.

Amazon claims it will be making Distance Assistant open-source to help other companies adapt to the coronavirus pandemic and keep their employees safe.

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The AI-powered tool is able to analyse CT scans for signs of COVID-19 infection to help quickly diagnose the novel coronavirus which has caused havoc around the world.

With heroic medical staff under more pressure than ever caring for the huge influx of people suffering with COVID-19 – in addition to all the other ailments they have to treat – such an AI-powered tool could help to free up significant amounts of time.

M3 has been testing the solution in Japan since the end of March; with the aim of deploying it across hundreds of locations. 

Hospitals will send CT scans to M3’s system which will then return the results with a 1-5 scale indicating the likelihood of COVID-19 pneumonia.

Alibaba’s system has been used in Chinese hospitals – including in Wuhan, the expected source of the COVID-19 outbreak – for a while now. The Chinese tech giant claims its AI can diagnose COVID-19 within 20 seconds with an accuracy of 90 percent or higher.

On average, a doctor takes around 20 minutes to make a diagnosis once a CT scan is available. M3 has found that the system typically diagnoses in under a minute.

While finding the accuracy to be relatively high, M3 reports the accuracy falls short of the 90 percent claimed by Alibaba. Even at 90 percent, 100 patients in every 1000 risk being misdiagnosed.

However, reading COVID-19 scans is reportedly even tricky for skilled physicians – especially as the virus is still relatively new. An AI-powered system which frees up clinical time is sure to be welcomed by all hospitals.

Catching the smaller signs of COVID-19 early could even help with providing treatment to those who need it before they get seriously ill.

This isn’t the first time AI has been looked to for assistance in tackling the COVID-19 pandemic.

Earlier this week, researchers from WVU Medicine and the Rockefeller Neuroscience Institute said they were able to predict the onset of COVID-19 symptoms three days early using AI to analyse data from Oura’s wearable rings.

Back in April, researchers from Carnegie Mellon University launched an AI-powered voice analysis system which aims to determine whether someone is suffering from COVID-19 using just a website.

While it seems likely we’re going to be living with COVID-19 in our lives for the foreseeable future, AI technologies look ready to step in and help.

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The researchers, from WVU Medicine and the Rockefeller Neuroscience Institute, first announced the potentially groundbreaking project in April.

At the time, the researchers found they could predict COVID-19 symptoms – including fever, cough, and fatigue – up to 24 hours before their onset.

“The holistic and integrated neuroscience platform developed by the RNI continuously monitors the human operating system, which allows for the accurate prediction of the onset of viral infection symptoms associated with COVID-19,” said Ali Rezai, M.D., executive chair of the WVU Rockefeller Neuroscience Institute.

“We feel this platform will be integral to protecting our healthcare workers, first responders, and communities as we adjust to life in the COVID-19 era.”

Participants in the study were asked to log neurological symptoms like stress and anxiety in an app. The Oura ring, meanwhile, automatically tracks physiological data like body temperature, heart rate, and sleep patterns.

“We are hopeful that Oura’s technology will advance how people identify and understand our body’s most nuanced physiological signals and warning signs, as they relate to infectious diseases like COVID-19,” explained Harpreet Rai, CEO of Oura Health.

“Partnering with the Rockefeller Neuroscience Institute on this important study helps fulfil Oura’s vision of offering data for the public good and empowering individuals with the personal insights needed to lead healthier lives.”  

Using an AI prediction model, the researchers have improved their ability to track COVID-19 symptoms from 24 hours before their onset to three days.

The accuracy rate for the current system is 90 percent. While impressive, that does mean 100 people in every 1000 patients could be misdiagnosed if such a system was widely rolled out.

This isn’t the only research into the use of wearables to help tackle the COVID-19 pandemic – Fitbit is also conducting a large study into whether its popular wearables can detect markers which may indicate that a user is infected with the novel coronavirus and should therefore quarantine and seek a professional test.

With the COVID-19 pandemic looking set to disrupt our lives for the foreseeable future, it seems AI and wearables provide some hope of diagnosing cases earlier, limiting reinfection, and helping people return to some degree of normality.

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Social distancing measures around the world appear to be having the desired effect. In many countries, the “curve” appears to be flattening with fewer deaths and hospital admissions per day.

No healthcare system in the world is prepared to handle a vast number of its population hospitalised at once. Even once relatively trivial ailments can become deadly if people cannot access the care they need. Until a vaccine is found, that’s why maintaining social distancing is vital even as lockdown measures ease.

With the curve now flattening, the conversation is switching to how lockdowns can be lifted safely. Contact-tracing apps, which keep track of everyone an individual passes and alerts them to self-isolate if they’ve been near anyone subsequently diagnosed with COVID-19, are expected to be key in easing measures.

MIT’s AI corroborates what many health officials are showing in their figures; that we should now be seeing new cases of COVID-19 levelling off in many countries.

“Our results unequivocally indicate that the countries in which rapid government interventions and strict public health measures for quarantine and isolation were implemented were successful in halting the spread of infection and prevent it from exploding exponentially,” the researchers wrote.

However, the situation could be similar to Singapore where lockdown measures almost completely flattened the curve before an early return to normal resulted in a massive resurgence in cases.

“Relaxing or reversing quarantine measures right now will lead to an exponential explosion in the infected case count, thus nullifying the role played by all measures implemented in the US since mid-March 2020.”

The team from MIT trained their AI using public data on COVID-19’s spread and how each government implemented various measures to contain it. It was trained on known data from January to March, and then was found to accurately predict the spread in April so far.

While the researchers’ work focused on COVID-19 epidemics in the US, Italy, South Korea, and Wuhan, there’s no reason to think that relaxing social distancing rules anywhere else in the world at this stage would be any less dire.

You can find the full paper from MIT here.

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