While quite rare, prescriptions that are incorrect – or react badly to a patient’s existing medications – can result in hospitalisation or even death.

In a blog post today, Alvin Rajkomar MD, Research Scientist and Eyal Oren PhD, Product Manager, Google AI, set out their work on using AI for medical predictions.

The AI is able to predict which conditions a patient is being treated for based on certain parameters. “For example, if a doctor prescribed ceftriaxone and doxycycline for a patient with an elevated temperature, fever and cough, the model could identify these as signals that the patient was being treated for pneumonia,” the researchers wrote.

In the future, an AI could step in if a medication that’s being prescribed looks incorrect for a patient with a specific condition in their current situation.

“While no doctor, nurse, or pharmacist wants to make a mistake that harms a patient, research shows that 2% of hospitalized patients experience serious preventable medication-related incidents that can be life-threatening, cause permanent harm, or result in death,” the researchers wrote.

“However, determining which medications are appropriate for any given patient at any given time is complex — doctors and pharmacists train for years before acquiring the skill.”

The AI was trained on an anonymised data set featuring around three million records of medications issued from over 100,000 hospitalisations.

In their paper, the researchers wrote:

“Patient records vary significantly in length and density of data points (e.g., vital sign measurements in an intensive care unit vs outpatient clinic), so we formulated three deep learning neural network model architectures that take advantage of such data in different ways: one based on recurrent neural networks (long short-term memory (LSTM)), one on an attention-based TANN, and one on a neural network with boosted time-based decision stumps.

We trained each architecture (three different ones) on each task (four tasks) and multiple time points (e.g., before admission, at admission, 24 h after admission and at discharge), but the results of each architecture were combined using ensembling.”

You can find the full paper in science journal Nature here.

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Babylon Health’s chatbot is available in the company’s GP at Hand app, a digital healthcare solution championed by health secretary Matt Hancock that was also integrated into Samsung Health since last year.

The chatbot aims to reduce the burden on GPs and A&E departments by automating the triage process to determine whether someone can treat themselves at home, should book an online or in-person GP appointment, or go straight to a hospital.

A Twitter user under the pseudonym of Dr Murphy first reached out to us back in 2018 alleging that Babylon Health’s chatbot was giving unsafe advice. Dr Murphy recently unveiled himself as Dr David Watkins and went public with his findings at The Royal Society of Medicine’s “Recent developments in AI and digital health 2020“ event in addition to appearing on a BBC Newsnight report.

Over the past couple of years, Dr Watkins has provided many examples of the chatbot giving dangerous advice. In one example, an obese 48-year-old heavy smoker patient who presented himself with chest pains was suggested to book a consultation “in the next few hours”. Anyone with any common sense would have told you to dial an emergency number straight away.

This particular issue has since been rectified but Dr Watkins has highlighted many further examples over the years which show, very clearly, there are serious safety issues.

In a press release (PDF) on Monday, Babylon Health calls Dr Watkins a “troll” who has “targeted members of our staff, partners, clients, regulators and journalists and tweeted defamatory content about us”.

According to the release, Dr Watkins has conducted 2,400 tests of the chatbot in a bid to discredit the service while raising “fewer than 100 test results which he considered concerning”.

Babylon Health claims that in just 20 cases did Dr Watkins find genuine errors while others were “misrepresentations” or “mistakes,” according to Babylon’s own “panel of senior clinicians” who remain unnamed.

Speaking to TechCrunch, Dr Watkins called Babylon’s claims “utterly nonsense” and questions where the startup got its figures from as “there are certainly not 2,400 completed triage assessments”.

Dr Watkins estimates he has conducted between 800 and 900 full triages, some of which were repeat tests to see whether Babylon Health had fixed the issues he previously highlighted.

The doctor acknowledges Babylon Health’s chatbot has improved and has issues around the rate of around one in three instances. In 2018, when Dr Watkins first reached out to us and other outlets, he says this rate was “one in one”.

While it’s one account versus the other, the evidence shows that Babylon Health’s chatbot has issued dangerous advice on a number of occasions. Dr Watkins has dedicated many hours to highlighting these issues to Babylon Health in order to improve patient safety.

Rather than welcome his efforts and work with Dr Watkins to improve their service, it seems Babylon Health has decided to go on the offensive and “try and discredit someone raising patient safety concerns”.

In their press release, Babylon accuses Watkins of posting “over 6,000” misleading attacks but without giving details of where. Dr Watkins primarily uses Twitter to post his findings. His account, as of writing, has tweeted a total of 3,925 times and not just about Babylon’s service.

This isn’t the first time Babylon Health’s figures have come into question. Back in June 2018, Babylon Health held an event where it boasted its AI beat trainee GPs at the MRCGP exam used for testing their ability to diagnose medical problems. The average pass mark is 72 percent. “How did Babylon Health do?” said Dr Mobasher Butt at the event, a director at Babylon Health. “It got 82 percent.”

Given the number of dangerous suggestions to trivial ailments the chatbot has given, especially at the time, it’s hard to imagine the claim that it beats trainee GPs as being correct. Intriguingly, the video of the event has since been deleted from Babylon Health’s YouTube account and the company removed all links to coverage of it from the “Babylon in the news” part of its website.

When asked why it deleted the content, Babylon Health said in a statement: “As a fast-paced and dynamic health-tech company, Babylon is constantly refreshing the website with new information about our products and services. As such, older content is often removed to make way for the new.”

AI solutions like those offered by Babylon Health will help to reduce the demand on health services and ensure people have access to the right information and care whenever and wherever they need it. However, patient safety must come first.

Mistakes are less forgivable in healthcare due to the risk of potentially fatal or lifechanging consequences. The usual “move fast and break things” ethos in tech can’t apply here. 

There’s a general acceptance that rarely is a new technology going to be without its problems, but people want to see that best efforts are being made to limit and address those issues. Instead of welcoming those pointing out issues with their service before it leads to a serious incident, it seems Babylon Health would rather blame everyone else for its faults.

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Using a machine learning algorithm, the MIT researchers were able to discover a new antibiotic compound which did not develop any resistance during a 30-day treatment period on mice.

The algorithm was trained using around 2,500 molecules – including about 1,700 FDA-approved drugs and a set of 800 natural products – to seek out chemical features that make molecules effective at killing bacteria. 

After the model was trained, the researchers tested it on a library of about 6,000 compounds known as the Broad Institute’s Drug Repurposing Hub.

“We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery,” explains Editor Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering.

“Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered.”

Antibiotic resistance is terrifying. Researchers have already discovered bacterias that are immune to current antibiotics and we’re very much in danger of illnesses that have become simple to treat becoming deadly once more.

Data from the Centers for Disease Control and Prevention (CDC) already indicates that antibiotic-resistant bacteria and antimicrobial-resistant fungi cause more than 2.8 million infections and 35,000 deaths a year in the United States alone.

“We’re facing a growing crisis around antibiotic resistance, and this situation is being generated by both an increasing number of pathogens becoming resistant to existing antibiotics, and an anaemic pipeline in the biotech and pharmaceutical industries for new antibiotics,” Collins says.

The recent coronavirus outbreak leaves many patients with pneumonia. With antibiotics, pneumonia is not often fatal nowadays unless a patient has a substantially weakened immune system. The current death toll for coronavirus would be much higher if antibiotic resistance essentially sets healthcare back to the 1930s.

MIT’s researchers claim their AI is able to check more than 100 million chemical compounds in a matter of days to pick out potential antibiotics that kill bacteria. This rapid checking reduces the time it takes to discover new lifesaving treatments and begins to swing the odds back in our favour.

The newly discovered molecule is called halicin – after the AI named Hal in the film 2001: A Space Odyssey – and has been found to be effective against E.coli. The team is now hoping to develop halicin for human use (a separate machine learning model has already indicated that it should have low toxicity to humans, so early signs are positive.)

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‘If You’re Under 50, You’ll Live Forever: Hello Transhumanism’ was the name of the session and featured Alex Rodriguez Vitello of the World Economic Forum and Stephen Dunne of Telefonica-owned innovation facility Alpha.

Transhumanism is the idea that humans can evolve beyond their current physical and mental limitations using technological advancements. In some ways, this is already happening.

Medical advancements have extended our lifespans and AI is helping to make further breakthroughs in areas such as cancer treatment.

Vitello notes how Dr Aubrey de Grey from the SENS Research Foundation has been able to extend the lifespan of mice threefold (Fun fact: Grey was an AI reseearcher before switching fields to biology.)

“That’s about 300 years in human years. And these mice are super happy, they’re like having sex and everything is great,” jokes Vitello.

Prosthetics, meanwhile, are enabling people to overcome their disabilities. Today, you can even be turned into a human compass with an implant that vibrates every time you face north.

CRISPR gene editing will one day help to eliminate disorders prior to birth. “You can eliminate cancer, muscular dystrophy, multiple sclerosis… all these things,” comments Vitello.

Artificial limbs will go beyond matching the abilities of natural body parts and provide things such as enhanced vision or superhuman strength beyond what even Arnie achieved in his prime.

These are exciting possibilities, but some transhumanist concepts are many years from becoming available. Even when they are, most enhancements will remain unaffordable for quite some time.

Cryogenics, the idea of being frozen to be revived years in the future, is one such example of something that’s possible today but unaffordable to most. One of the biggest companies in the field is Alcor if you’re willing to part with $200,000.

In answer to whether he agreed with the panel’s title, Dunne responded that a better question to ask is whether the first person is alive that will live forever. On that basis, he believes they might be.

“If you’re [Amazon CEO] Jeff Bezos, maybe,” commented Dunne. “If you put all your resources towards that.”

One concept is that we’ll be able to live forever virtually through storing a digital copy of our brains. American inventor and futurist Ray Kurzweil wants his brain to be downloaded and uploaded elsewhere when he dies.

“What’s more, he [Ray] has all these recordings of his father and he wants to take all of this information and put it on a computer brain to see if he can reproduce the essence of his father,” says Vitello.

This kind of thing requires the ability to emulate the brain. While huge strides in computing power are being made, we’re some way off from that level of processing power.

“I met Ray recently and he thinks of it as a computer scientist, that if we have enough computing power we can simulate the brain,” comments Dunne. “I think we’re so far off understanding how the brain works this is just wrong at the moment.”

Even what conciousness is still eludes researchers. Only last year was a whole new neuron was discovered which goes to show how little we know about the brain at this point.

“The company I used to work for [Neurolectrics] has a project on measuring consciousness, but just the level of it,” Dunne continues. “We just don’t know how this stuff works at a very fundamental level.”

When asked how far along ‘the loading bar’ we are towards brain emulation, Dunne said he’d put it at somewhere around one percent. However, things such as stimulating the brain to improve memory retention or boost certain abilities he believes is a lot closer.

That isn’t without its own challenges. Dunne explains how it’s almost impossible for someone able-sighted to learn braille as not enough brain power is dedicated to the task.

“If you enhance one feature, you kind of have to take that processing power from somewhere else,” he says. “To learn braille you need to be blind as otherwise you’re using your visual cortex and there’s not enough computing power for the task.”

Dunne then goes on to note how AI could help to speed up breakthroughs that are difficult for us to comprehend today: “If we do invent artificial general intelligence, it might figure out all we need to know about the brain to do this within the next 30 years.”

AI is keeping the dream alive, but it seems unlikely that many – if any – under 50 will be living forever. At least we can look forward to some transhumanist enhancements in the coming years.

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Conditions such as Alzheimer’s, Parkinson’s, Huntington’s, and cystic fibrosis are suspected to be caused by misfolded proteins. Being able to predict a protein’s shape enables a greater understanding of its role within the body.

Previous techniques used for determining the shapes of proteins – such as cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography – takes years and costs tens of thousands of dollars per structure.

AI, the researchers hope, will enable target shapes to be modelled from scratch without requiring previously solved proteins to be used as templates.

DeepMind calls their AI-powered folding efforts AlphaFold.

AlphaFold uses two different methods to construct predictions of protein structures:

1. 1. The first method repeatedly replaces pieces of a protein structure with new protein fragments, building on a technique commonly used in structural biology. A neural network invents new fragments.

1. The second method is called ‘gradient descent’ which is a mathematical technique applied to entire protein chains rather than pieces and makes small, incremental improvements.

Image Credit: DeepMind

DeepMind says its work is a successful demonstration of how AI can reduce the complexity of tasks such as protein folding; speeding up the diagnosis and treatment of some of the world’s most debilitating conditions.

In a contest organised by the Protein Structure Prediction Centre, AlphaMind was judged the winner among a total 98 algorithms by predicting the shapes of 25 out of 43 proteins. The runner-up, in comparison, could only predict three of the 43 proteins.

“For us, this is a really key moment,” said Demis Hassabis, co-founder and CEO of DeepMind. “This is a lighthouse project, our first major investment in terms of people and resources into a fundamental, very important, real-world scientific problem.”

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Machines are becoming increasingly smart thanks to artificial intelligence, but they still remain cold, logical, and lacking emotion. Worse still, they have a bias problem.

“We are teaching the machine to synthetically emulate emotional intelligence to better relate to how you and I feel,” states Nevarez.

“So many exciting applications present themselves to enhance healthcare analytics, market assessment, consumer and voter sentiment, and delivering customised content in the Internet of Things.”

Nevarez recounted a time he was out with his son and they saw a person fall. His son laughed, but – when Nevarez explained the person could be hurt – his son became empathetic. Over time, his son recognised when to show empathy.

AI learns from patterns, and Nevarez believes – much like his son – they can be taught with empathetic values.

“Teaching a machine to feel, is just as important as teaching a machine to think,” says Nevarez. “Or we end up with a world heavily-biased by the engineers that program those AIs.”

Emotional Analysis

The company started with political forecasting. Politics, as we all know, is very much driven by sentiment and ideologies.

In the past couple of years, there’s been some major elections and decisions not many saw coming.

Navarez applied BPU’s emotional computing engine to the US Presidential Elections for a personal project. Based on its analysis, Donald Trump was going to win.

“I wanted to see if our emotional computing engine would come close to predicting the outcome of the election,” recalls Navarez. “After watching the election for about eight weeks, and not really getting a ton of data – because it was just me and I didn’t have the computing resources of the company – I came up with the prediction that Donald Trump was going to win.”

“We called our engineers and said there’s something wrong with our algorithms, it’s predicting Trump is going to win.”

A week later, Donald Trump won.

There is very little polling done via telephone anymore, it’s mostly online. This has increased accuracy as people are more honest online.

“People tend to be more honest when they’re flaming someone on Twitter,” comments Nevarez. “When people ask [in person] ‘How do you feel about this candidate?’ then people want to be nice, they don’t want to hurt someone’s feelings.”

For a customer this time, BPU attempted to predict the Korean elections.

Twitter is less used in South Korea and Nevarez was sceptical it would be accurate. However, yet again, it was able to correctly predict the outcome leading to the election of President Moon Jae-In.

BPU even released its results on the morning of the election, days before traditional pollsters. The worst margin of error was just two percent.

For a final example, BPU showed how it correctly ranked the results of the Nevada US House District Republican Primary election.

The examples prove BPU’s sentiment analysis works. However, it’s understanding an individual’s emotions and helping to alter them (for the better) which could have the greatest impact.

Seth Grimes, Principal Analyst for Alta Plana specialising in natural language processing (NLP), text analytics, and sentiment analysis, states: “Automated emotion understanding — emotion AI — is now a must-have capability for consumer marketing and public-facing campaigns, including electoral campaigns…”

Building Emotional Apps

AI will, and is, revolutionising healthcare. It’s also one of the areas where empathy is most needed.

aiMei is an app created by BPU which offers personality tests and mood analysis in a chatbot-like interface. The company made a version of it for the medical industry where a physician can train it for a patient’s needs.

The app could ask whether a person has taken their ibuprofen yet, for example. Being a tablet known for causing stomach irritation when taken on an empty stomach, it could ask whether the individual has eaten yet or not.

A bit later it could ask if the person wants a snack, pre-programmed with those available that day, so a nurse doesn’t have to go and check with each patient.

Finally, the patient may be asked to provide their mood – or how they’re feeling – an hour or so after, to know whether the medication is working or not.

Physicians have reportedly said to BPU that, while they can go monitor things like temperature, they’re unable to keep a record of a patient’s mental wellbeing – but they’d like to.

Salim Hariri, Ph.D., co-director of the Natural Science Foundation and The Center for Autonomic Computing at the University of Arizona, recently stated: “Among many other applications, BPU’s AEI technology shows great potential for healthcare advances in patient emotional and critical assessment.”

The company also has a smartwatch app which can detect when a person’s heart rate is accelerating and look for the potential reason.

By collaborating with a heart surgeon, the smartwatch app is able to accurately predict five minutes before a cardiac arrest is going to occur. This means health professionals can be alerted earlier to begin preparations which could be life-saving.

Outside healthcare, BPU has also produced a personalised news app called Neil which determines a user’s individual emotional reaction to articles in order to serve up more or less of similar coverage.

All of these apps, Nevarez says, is providing the company with a good look at the human emotional genome and helping it to create frameworks that help anyone build empathetic apps.

Find out more about AI Expo and the next event here.

Do you think we should be teaching AI to feel as well as think? Let us know your thoughts in the comments.

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AI News first covered GP at Hand in November last year.

“GP at Hand is a window into what the NHS of the future will look like,” said Dr Howard Freeman MBE, senior GP. “When innovative NHS GPs embrace Babylon’s technology to make life better for their patients, the sky is the limit.”

The service uses AI to determine if a patient’s symptoms require further attention before putting them in touch with a GP using video chat if necessary.

Where appropriate, prescriptions can be sent automatically to a pharmacy of choice — or a patient can be booked in for a physical examination at a practice.

Dr. Ali Parsa, Babylon’s Founder & CEO, says:

“Babylon’s mission is to make healthcare accessible and affordable and to put it into the hands of everyone on Earth. Samsung’s vision for empowering individuals and transforming healthcare, partnered with the company’s illustrious history of technological innovation, constant focus on customer satisfaction and truly global reach makes it a perfect fit with our values and mission.

It’s very exciting to know that millions of Samsung users will soon be able to better manage their health using Babylon’s services as we deliver personal health assessments and treatment advice via their Samsung Galaxy devices.”

Samsung will be integrating Babylon Health’s service into the built-in Samsung Health app on compatible Galaxy devices. The service will not be free: users can decide between a £50 per year subscription, or pay £25 for a one-off appointment.

Kyle Brown, Head of Technology and Services at Samsung UK, adds:

“We’re excited to be welcoming ‘Ask an Expert, powered by Babylon’ to the Samsung Health app. Now our customers will be able to look after their health from wherever they are – whether it’s checking a symptom or talking to a doctor – all within a few simple taps.

The availability of the Babylon service within the app is another milestone for Samsung as we move towards a more connected, healthy world.”

Health startup Babylon has been expanding rapidly as people look for alternatives to overburdened traditional health services.

Dame Barbara Hakin, Former GP and National Director in NHS England, comments:

“I know just how difficult times are for GPs these days and how busy they are. GP at Hand, in addition to being very convenient for patients, can help the service given the recruitment crisis we know is facing us.

This technology can take more of the strain and ensure the best information and insight is available ahead of consultations which will then relieve some of the pressure on hard-pressed clinicians.”

While the service is launching in the UK, Babylon Health is looking to expand its partnership with Samsung worldwide.

Babylon Health recently signed a deal with social giant WeChat in China to offer its services in the country; showing its desire to make healthcare more accessible to everyone around the world.

One day, it’s not hard to imagine a subscription to a service like GP at Hand being able to quickly connect patients with local doctors for advice and treatment even while travelling in other countries. That could offer a lot of peace of mind.

What are your thoughts on the partnership?

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Cancer has a higher successful treatment rate the earlier it’s diagnosed. The later the diagnosis, the greater the risk of death or long-term debilitating effects.

In her speech, Mrs May will say:

“Late diagnosis of otherwise treatable illnesses is one of the biggest causes of avoidable deaths.

The development of smart technologies to analyse great quantities of data quickly, and with a higher degree of accuracy than is possible by human beings, opens up a whole new field of medical research and gives us a new weapon in our armoury in the fight against disease.

Achieving this mission will not only save thousands of lives, it will incubate a whole new industry around AI-in-healthcare. It will create high-skilled science jobs across the country – drawing on existing centres of excellence in places like Edinburgh, Oxford, and Leeds – and help to grow new ones.”

At least 50,000 people a year suffering from lung, prostate, ovarian, or bowel cancer will be diagnosed earlier due to AI, May will claim.

To achieve this goal, researchers will require access to large amounts of medical records to cross-reference patients’ lifestyles, genetics, and prior conditions to highlight when individuals are most at risk.

The UK’s National Health Service (NHS) has vast amounts of data. Every time a patient visits a service anywhere in the country, a record is made.

A patient’s medical record can include:

• • treatments received or ongoing

• • information about allergies

• • current medication(s)

• • any reactions to medications in the past

• • any known long-term conditions, such as diabetes or asthma

• • medical test results such as blood tests, allergy tests, and other screenings

• • any clinically relevant lifestyle information, such as smoking, alcohol or weight

• • personal data, such as age, name, and address

• • consultation notes, which a doctor takes during an appointment

• • hospital admission records, including the reason

• • hospital discharge records, which will include the results of treatment and whether any follow-up appointments or care are required

• • X-rays

• photographs and image slides, such as MRI scans or CT scans

How this data is shared and used to improve medical care remains a controversial topic. For example, the NHS’ sharing of data with Google-owned DeepMind has often come under scrutiny.

An independent panel last year found the deal between DeepMind and the Royal Free NHS Foundation Trust to develop an app for diagnosing kidney disease was ‘illegal’ and did not do enough to safeguard patient data.

Theresa May’s party, the Conservatives, have also faced widespread criticism over under-funding and privatisation of the NHS — leading to increased staff pressure and longer waiting times for patients.

Two-thirds of NHS trusts reported having at least one cancer patient waiting more than six months last year, while almost seven in 10 (69%) trusts said they had a worse longest wait than in 2010. One cancer patient waited 541 days for treatment.

If employed correctly, the automation offered by AI has the potential to greatly reduce staff pressure and improve patient care.

“Earlier detection and diagnosis could fundamentally transform outcomes for people with cancer, as well as saving the NHS money,” comments Sir Harpal Kumar, CEO of Cancer Research. “Advances in detection technologies depend on the intelligent use of data and have the potential to save hundreds of thousands of lives every year.”

“We need to ensure we have the right infrastructure, embedded in our health system, to make this possible.”

What are your thoughts on the use of AI in healthcare?

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Medical imaging is so important because it offers a way to pick up and detect problems as early as possible without intrusive methods. GE Healthcare and Nuance have chosen to partner with Nvidia for use of their deep learning platform.

Kimberly Powell, Vice President of Healthcare at NVIDIA, said: “Medical imaging is an essential tool for delivering the best healthcare, and now we have the opportunity to massively enhance it with AI.”

GE Healthcare

To begin with, Nvidia’s AI computing platform will be deployed across GE Healthcare’s 500,000 imaging devices around the world and use ‘Revolution Frontier CT’ which Nvidia claims is twice as fast at image processing than its predecessor.

The company will also power GE Healthcare’s analytics platform ‘Applied Intelligence’ to help ensure the faster deployment of deep-learning algorithms for future medical instruments.

Nuance

Next up is the partnership with Nuance who are announcing their ‘AI Marketplace for Diagnostic Imaging’ which is built on Nvidia’s deep learning platform.

The aim for the marketplace is to enable radiologists to get involved with building potentially life-saving algorithms for use in clinics around the world.

“Transforming the delivery of patient care and combating disease starts with the most advanced technologies being readily available when and where it counts – in every reading room, across the United States,” said Peter Durlach, senior vice president, Healthcare at Nuance.

“Our AI Marketplace will bring together the leading technical, research and healthcare minds to create a collection of image processing algorithms that, when made accessible to the wide array of radiologists who use our solutions daily, has the power to exponentially impact outcomes and further drive the value of radiologists to the broader care team.”

Nuance claims around 70 percent of radiologists use their image-sharing and reporting solutions, and the deployment of this marketplace will help to improve the workflow of radiologists for quicker detection and quantification of key clinical findings.

Nvidia’s Digits developer tool will be updated to provide developers with a way to publish their algorithms directly to Nuance PowerShare.

The announcements were made during the annual meeting of the Radiological Society of North America (RSNA), in Chicago, which was attended by more than 50,000 professionals.

Find the rest of our coverage of AI for healthcare purposes here.

Are you impressed with Nvidia’s new partnerships to use AI for improving radiology?

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