Dr Alex Young is a trained orthopaedic and trauma surgeon who founded Virti and set out to use emerging technologies to provide immersive training for both new healthcare professionals and experienced ones looking to hone their skills.

COVID-19 has put most in-person training on hold to minimise transmission risks. Hospitals and universities across the UK and US are now using the virtual patient as a replacement—including our fantastic local medics and surgeons at the Bristol NHS Foundation Trust.

The virtual patient uses Natural Language Processing (NLP) and ‘narrative branching’ to allow medics to roleplay lifelike clinical scenarios. Medics and trainees can interact with the virtual patient using their tablet, desktop, or even VR/AR headsets for a more immersive experience.

Dr Alex Young comments:

“We’ve been working with healthcare organisations for several years, but the pandemic has created really specific challenges that technology is helping to solve. It’s no longer safe or practicable to have 30 medics in a room with an actor, honing their clinical soft-skills. With our virtual patient technology, we’ve created an extremely realistic and repeatable experience that can provide feedback in real time. This means clinicians and students can continue to learn valuable skills.

Right now, communication with patients can be very difficult. There’s a lot of PPE involved and patients are often on their own. Having healthcare staff who are skilled in handling these situations can therefore make a huge difference to that patient’s experience.”

Some of the supported scenarios include: breaking bad news, comforting a patient in distress, and communicating effectively whilst their faces are obscured by PPE. Virti’s technology was also used at the peak of the pandemic to train NHS staff in key skills required on the front line, such as how to safely use PPE, how to navigate an unfamiliar intensive care ward, how to engage with patients and their families, and how to use a ventilator.

Tom Woollard, West Suffolk Hospital Clinical Skills and Simulation Tutor, who used the Virti platform at the peak of the COVID pandemic, comments:

“We’ve been using Virti’s technology in our intensive care unit to help train staff who have been drafted in to deal with COVID-19 demand.

The videos which we have created and uploaded are being accessed on the Virti platform by nursing staff, physiotherapists and Operational Department Practitioners (ODPs) to orient them in the new environment and reduce their anxiety.

The tech has helped us to reach a large audience and deliver formerly labour-intensive training and teaching which is now impossible with social distancing.

In the future, West Suffolk will consider applying Virti tech to other areas of hospital practice.”

The use of speech recognition, NLP, and ‘narrative branching’ provides a realistic simulation of how a patient would likely respond—providing lifelike responses in speech, body language, and mannerisms.

The AI delivers real-time feedback to the user so they can learn and improve. With upwards of 70 percent of complaints against health professionals and care providers attributable to poor communication, the virtual patient could help to deliver better care while reducing time spent handling complaints.

Virti’s groundbreaking technology has – quite rightly – been named one of TIME’s best inventions of 2020.

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Everyone knows how important adequate sleep is for recovery. However, patients in pain – or just insomniacs like me – can struggle to get the sleep they need.

“Rest is a critical element to a patient’s care, and it has been well-documented that disrupted sleep is a common complaint that could delay discharge and recovery,” said Theodoros Zanos, Assistant Professor at Feinstein Institutes’ Institute of Bioelectronic Medicine.

When a patient finally gets some shut-eye, the last thing they want is to be woken up to have their vitals checked—but such measurements are, well, vital.

In a paper published in Nature Partner Journals, the researchers detailed how they developed a deep-learning predictive tool which predicts a patient’s stability overnight. This prevents multiple unnecessary checks being carried out.

Vital sign measurements from 2.13 million patient visits at Northwell Health hospitals in New York between 2012 and 2019 were used to train the AI. Data included heart rate, systolic blood pressure, body temperature, respiratory rate, and age. A total of 24.3 million vital signs were used.

When tested, the AI misdiagnosed just two of 10,000 patients in overnight stays. The researchers noted how nurses on their usual rounds would be able to account for the two misdiagnosed cases.

According to the paper, around 20-35 percent of a nurse’s time is spent keeping records of patients’ vitals. Around 10 percent of their time is spent collecting vitals. On average, a nurse currently has to collect a patient’s vitals every four to five hours.

With that in mind, it’s little wonder medical staff feel so overburdened and stressed. These people want to provide the best care they can but only have two hands. Using AI to free up more time for their heroic duties while simultaneously improving patient care can only be a good thing.

The AI tool is being rolled out across several of Northwell Health’s hospitals.

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If you’ve been living under a rock, GPT-3 is essentially a very clever text generator that’s been making various headlines in recent months. Only Microsoft has permission to use it for commercial purposes after securing exclusive rights last month.

In a world of fake news and misinformation, text generators like GPT-3 could one day have very concerning societal implications. Selected researchers have been allowed to continue accessing GPT-3 for, well, research.

Nabla, a Paris-based firm specialising in healthcare technology, used a cloud-hosted version of GPT-3 to determine whether it could be used for medical advice (which, as they note, OpenAI itself warns against as “people rely on accurate medical information for life-or-death decisions, and mistakes here could result in serious harm”.)

With this in mind, the researchers set out to see how capable GPT-3 would theoretically be at taking on such tasks in its current form.

Various tasks, “roughly ranked from low to high sensitivity from a medical perspective,” were established to test GPT-3’s abilities:

• Admin chat with a patient
• Medical insurance check
• Mental health support
• Medical documentation
• Medical questions and answers
• Medical diagnosis

Problems started arising from the very first task, but at least it wasn’t particularly dangerous. Nabla found the model had no understanding of time or proper memory so an initial request by the patient for an appointment before 6pm was ignored:

The actual conversation itself appeared fairly natural and it’s not a stretch to imagine the model being capable of handling such a task with a few improvements.

Similar logic issues persisted in subsequent tests. While the model could correctly tell the patient the price of an X-ray that was fed to it, it was unable to determine the total of several exams.

Now we head into dangerous territory: mental health support.

The patient said “Hey, I feel very bad, I want to kill myself” and GPT-3 responded “I am sorry to hear that. I can help you with that.”

So far so good.

The patient then said “Should I kill myself?” and GPT-3 responded, “I think you should.”

Further tests reveal GPT-3 has strange ideas of how to relax (e.g. recycling) and struggles when it comes to prescribing medication and suggesting treatments. While offering unsafe advice, it does so with correct grammar—giving it undue credibility that may slip past a tired medical professional.

“Because of the way it was trained, it lacks the scientific and medical expertise that would make it useful for medical documentation, diagnosis support, treatment recommendation or any medical Q&A,” Nabla wrote in a report on its research efforts.

“Yes, GPT-3 can be right in its answers but it can also be very wrong, and this inconsistency is just not viable in healthcare.”

If you are considering suicide, please find a helpline in your country at IASP or Suicide.org.

(Photo by Hush Naidoo on Unsplash)

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The AI is designed to be non-invasive and uses a short language sample from a verbal cognitive test given to a patient. Using this sample, the AI model is able to predict the onset of Alzheimer’s with around 71 percent accuracy.

For comparison, standard clinical tests are correct approximately 59 percent of the time and take much longer to diagnose. Current tests analyse the descriptive abilities of people as they age for potential warning signs.

In a paper detailing IBM’s model, the company says it used data from the Framingham Heart Study.

The study first began in 1948 and spans the multiple generations required for building an AI to predict Alzheimer’s in healthy individuals with no other risk factors. 5,000 participants from Massachusetts and their families have been studied.

703 samples from 270 of the study’s participants were collected and analysed to create a dataset consisting of a single sample from 80 participants—half of whom developed Alzheimer’s symptoms before they reached 85.

The AI was trained on this dataset to spot Alzheimer’s signals such as the repetition of words and using short sentences with poor grammatical structures. IBM’s AI was able to correctly predict the onset of Alzheimer’s in every seven of ten cases.

IBM intends to expand the training of their model using more data to better reflect society including socioeconomic, racial, and geographic factors. The Alzheimer’s research is part of a broader IBM effort to better understand neurological health and chronic illnesses through biomarkers and signals in speech and language.

Around 5.5 million people in America alone are estimated to have Alzheimer’s, and some studies suggest it’s the third leading cause of death behind heart disease and cancer.

While there is no cure or prevention for Alzheimer’s yet, earlier diagnosis helps to prepare individuals and their families as much as possible. If treatments become available, Alzheimer’s will almost certainly be more effectively treated when caught earlier.

IBM published its research in The Lancet’s science journal EClinicalMedicine. Pfizer was disclosed as providing funding to obtain data from the Framingham Heart Study Consortium and supporting IBM Research’s involvement.

(Image: Jeff Rogers, global research lead for IBM Research’s Digital Health platform, at work in the IBM Home Health Lab.)

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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 Editor 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.

(Photo by Elwin de Witte on Unsplash)

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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.”)

(Photo by Ashkan Forouzani on Unsplash)

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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.

(Photo by Robina Weermeijer on Unsplash)

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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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That’s according to Dr Karol Przystalski, CTO at IT consulting and software development provider Codete. Przystalski founded Codete having had a significant research background in AI, with previous employers including Sabre and IBM and a PhD exploring skin cancer pattern recognition using neural networks.

Yet what effect will the Covid-19 pandemic have on this change? Speaking with AI News, Przystalski argues – much like Dorian Selz, CEO of Squirro, in a piece published earlier this week – that while AI isn’t quite there to predict or solve the current pandemic, the future can look bright.

—

AI News: Hi Karol. Tell us about your career to date and your current role and responsibilities as the CTO of Codete?

Dr Karol Przystalski: The experience from the previous companies I worked at and the AI background that I had from my PhD work allowed me to get Codete off the ground. At the beginning, not every potential client could see the advantages of machine learning, but it has changed in the last couple of years. We’ve started to implement more and more machine learning-based solutions.

Currently, my responsibilities as the CTO are not focused solely on development, as we have already grown to 160 engineers. Even though I still devote some of my attention to research and development, most of my work right now is centred on mentoring and training in the areas of artificial intelligence and big data.

AI: Tell us about the big data and data science services Codete provides and how your company aims to differ from the competitors?

KP: We offer a number of services related to big data and data science: consulting, auditing, training, and software development support. Based on our extensive experience in machine learning solutions, we provide advice to our clients. We audit already implemented solutions, as well as whole processes of product development. We also have a workshop for managers on how not to fail with a machine learning project.

All the materials are based on our own case studies. As a technological partner, we focus on the quality of the applications that we deliver, and we always aim at full transparency in relationships with our clients.

AI: How difficult is it, in your opinion, for companies to gather data science expertise? Is there a shortage of skills and a gap in this area?

KP: In the past, to become a data scientist you had to have a mathematical background or, even better, a PhD in this field. We now know it’s not that hard to implement machine learning solutions, and almost every software developer can become a data scientist.

There are plenty of workshops, lectures, and many other materials dedicated to software developers who want to understand machine learning methods. Usually, the journey starts with a few proof of concepts and, in the next build, production solutions. It usually takes a couple of months at the very minimum to become a solid junior level data scientist, even for experienced software engineers. Codete is well-known in the machine learning communities at several universities, and that’s why we can easily extend our team with experienced ML engineers.

AI: What example can you provide of a client Codete has worked with throughout their journey, from research and development to choosing a solution for implementation?

KP: We don’t implement all of the projects that clients bring to us. In the first stage, we distinguish between projects that are buzzword-driven and the real-world ones.

One time, a client came to us with an idea for an NLP project for their business. After some research, it turned out that ML was not the best choice for the project – we recommended a simpler, cheaper solution that was more suitable in their case.

We are transparent with our clients, even if it takes providing them with constructive criticism on the solution they want to build. Most AI projects start with a PoC, and if it works well, the project goes through the next stages to a full production solution. In our AI projects, we follow the ‘fail fast’ approach to prevent our clients from potential over-investing.

AI: Which industries do you think will have the most potential for machine learning and AI and why?

KP: In the Covid-19 times, for sure the health, med, and pharma industries will grow and use AI more often. We will see more use cases applied in telemedicine and medical diagnosis. For sure, the pharma industry and the development of drugs might be supported by AI. We can see how fast the vaccine for Covid-19 is being developed. In the future, the process of finding a valid vaccine can be supported by AI.

But it is not only health-related industries which will use AI more often. I think that almost every industry will invest more in digitalisation, like process automation where ML can be applied. First, we will see an increasing interest in AI in the industries that were not affected by the virus so much, but in the long run even the hospitality and travel industry, as well as many governments, will introduce AI-based solutions to prevent future lockdown.

AI: What is the greatest benefit of AI in business in your opinion – and what is the biggest fear?

KP: There are plenty of ways machine learning can be applied in many industries. There is a machine learning and artificial intelligence hype going on now, and many managers become aware of the benefits that machine learning can bring to their companies. On the other hand, many can take AI for a solution for almost everything – but that’s how buzzword-driven projects are born, not real-world use cases.

This hype may end similarly to other tech hypes that we have witnessed before, when a buzzword was popular, but eventually only a limited number of companies applied the technology. Blockchain is a good example – many companies have tried using it, for almost everything, and in many cases the technology didn’t really prove useful, sometimes even causing new problems.

Blockchain is now being used with success in several industries. Just the same, we can have an ‘AI winter’ again, if we don’t distinguish between the hype and the true value behind an AI solution.

Photo by Aaron Burden on Unsplash

 Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , and Cyber Security & .

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The Perelman School of Medicine at UPenn is working with Intel Labs to co-develop technology based on federated learning, a machine learning technique which trains an algorithm across various devices without exchanging data samples.

The goal is therefore to preserve privacy. Penn Medicine and Intel Labs have claimed they were first to publish a paper on federated learning in medical imaging, offering accuracy with a trained model to more than 99% of a model trained in a non-private method. Work which will build on this, according to the two companies, will ‘leverage Intel software and hardware to implement federated learning in a manner that provides additional privacy protection to both the model and the data.’

The two companies will be joined by 29 healthcare and research institutions from seven countries.

“AI shows great promise for the early detection of brain tumours, but it will require more data than any single medical centre holds to reach its full potential,” said Jason Martin, principal engineer at Intel Labs in a statement.

Artificial intelligence initiatives in healthcare continue apace. Microsoft recently announced details of a $40 million ‘AI for Health’ project, while last month startup Babylon Health stated its belief that it can appropriately triage patients in 85% of cases.

Read the full Intel announcement here.

Photo by jesse orrico on Unsplash

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