The AI tool, called Mia, was piloted alongside NHS clinicians in the UK and analysed the mammograms of over 10,000 women. 

Most of the participants were cancer-free, but the AI successfully flagged all of those with symptoms of breast cancer—as well as an additional 11 cases that the doctors failed to identify. Of the 10,889 women who participated in the trial, only 81 chose not to have their scans reviewed by the AI system.

The AI tool was trained on a dataset of over 6,000 previous breast cancer cases to learn the subtle patterns and imaging biomarkers associated with malignant tumours. When evaluated on the new cases, it correctly predicted the presence of cancer with 81.6 percent accuracy and correctly ruled it out 72.9 percent of the time.

Breast cancer is the most common cancer in women worldwide, with two million new cases diagnosed annually. While survival rates have improved with earlier detection and better treatments, many patients still experience severe side effects like lymphoedema after surgery and radiotherapy.

Researchers are now developing the AI system further to predict a patient’s risk of such side effects up to three years after treatment. This could allow doctors to personalise care with alternative treatments or additional supportive measures for high-risk patients.

The research team plans to enrol 780 breast cancer patients in a clinical trial called Pre-Act to prospectively validate the AI risk prediction model over a two-year follow-up period. The long-term goal is an AI system that can comprehensively evaluate a patient’s prognosis and treatment needs.

(Photo by Angiola Harry)

See also: NVIDIA unveils Blackwell architecture to power next GenAI wave 

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The guidance, titled ’Validation framework for the use of AI within healthcare – Specification (BS 30440),’ aims to bolster confidence among clinicians, healthcare professionals, and providers regarding the safe, effective, and ethical development of AI tools.

As the global debate on the appropriate use of AI continues, this auditable standard targets products primarily designed for healthcare interventions, diagnoses, and health condition management.

Jeanne Greathouse, Global Healthcare Director at BSI, said:

“This standard is highly relevant to organisations in the healthcare sector and those interacting with it. As AI becomes the norm, it has the potential to be transformative for healthcare.

With the onset of more innovative AI tools, and AI algorithms’ ability to digest and accurately analyse copious amounts of data, clinicians and health providers can efficiently make informed diagnostic decisions to intervene, prevent, and treat diseases, ultimately improving patients’ quality of life.”

According to forecasts, the global healthcare AI market is expected to surpass $187.95 billion by 2030. However, healthcare providers and clinicians may face challenges in assessing AI products due to time and budget constraints or a lack of in-house capabilities. 

The BS 30440 specification seeks to aid decision-making processes by providing criteria for evaluating healthcare AI products, including clinical benefit, performance standards, safe integration into clinical environments, ethical considerations, and equitable social outcomes.

The standard covers a wide range of healthcare AI products, including regulated medical devices like software used for medical purposes, imaging software, patient-facing products like AI-powered smartphone chatbots, and home monitoring devices. It applies to products and technologies utilising AI elements – including machine learning – and is relevant to both AI system suppliers and product auditors.

The development of this specification involved collaboration among a panel of experts, including clinicians, software engineers, AI specialists, ethicists, and healthcare leaders. The guidance draws from existing literature and best practices, translating complex functionality assessments into an auditable framework for AI system conformity.

Healthcare organisations will be able to mandate BS 30440 certification in their procurement processes to ensure adherence to these recognized standards.

Scott Steedman, Director General for Standards at BSI, commented:

“The new guidance can help build digital trust in cutting-edge tools that represent enormous potential benefit to patients, and the professionals diagnosing and treating them.

AI has the potential to shape our future in a positive way and we all need confidence in the tools being developed, especially in healthcare.

This specification, which is auditable, can help guide everyone from doctors to healthcare leaders and patients to choose AI products that are safe, effective, and ethically produced.”

The specification addresses the need for an agreed validation framework for AI development and clinical evaluation in healthcare. It builds on a framework initially piloted at Guy’s and St. Thomas Cancer Centre and later revised through discussions with stakeholders involved in AI and machine learning.

With the publication of this guidance, BSI seeks to instil confidence in AI products used in healthcare and empower doctors, healthcare leaders, and patients to make informed and ethical choices for improved patient care and overall societal benefit.

As AI continues to shape the future of healthcare, adherence to recognised standards will play a vital role in ensuring the safe and effective integration of AI technologies in medical practice.

(Photo by Owen Beard on Unsplash)

See also: AI regulation: A pro-innovation approach – EU vs UK

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LabGenius is currently focused on discovering new treatments for cancer and inflammatory diseases. The firm combines AI, lab automation, and synthetic biology for its potentially life-saving work.

Until now, the company has been using traditional GPUs for its workloads. LabGenius reports that switching to Graphcore’s IPUs in cloud instances from Cirrascale Cloud Services enabled its training of models to be reduced from one month to around two weeks.

“Previously we used GPUs and it took us about a month to have a functioning model of all the proteins that are out there,” said Dr Katya Putintseva, a Machine Learning Advisor to LabGenius.

“With Graphcore, we reduced the turnaround time to about two weeks, so we can experiment much more rapidly and we can see the results quicker.”

Specifically, LabGenius is using IPUs from Bristol, UK-based Graphcore to train a BERT Transformer model on a large data set of known proteins to predict masked amino acids. This, the company says, enables the model to effectively learn the basic biophysics of proteins.

“[The system] is looking across different features we could change about the molecule — from point mutations of simpler constructs to the overall composition and topology of multi-module proteins,” explained Tom Ashworth, Head of Technology at LabGenius.

“It’s making suggestions about what to design next… to learn about a change in the input and how that maps to a change in the output.”

One in two people now develop cancer in their lifetime. Current treatments often cause much suffering themselves and, while survival rates for most forms are increasing, only around 50 percent survive for ten years or more.

AI will help to find new cancer treatments that cause less suffering and greatly increase the odds of long-term survivability. However, while discovering new cancer treatments is the current focus of LabGenius, the company notes how the principles can be applied more widely to find new treatments for other horrible diseases that plague mankind.

“Graphcore has changed what we’re able to do, accelerating our model training time from weeks to days,” adds Ashworth.

“For our data scientists, that’s really transformative. They can move much more at the speed they think.”

(Photo by National Cancer Institute on Unsplash)

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Breakthroughs in medical AIs are helping to reduce patient suffering, the likelihood and/or severity of long-term complications, and even save lives across a number of ailments.

Some of the benefits from medical AI breakthroughs are achieved through improved understanding leading to better treatment, while others are due to reducing the amount of time healthcare professionals have to spend on repetitive tasks.

Over 100,000 people in the UK suffer from a stroke per year; with over 32,000 deaths as a result. NHS SBS sought out how AI can help tackle one of the UK’s leading causes of death and disability.

Adam Nickerson, NHS SBS Senior Category Manager – Digital & IT, said:

“This use of AI is a prime example of how new technologies have the potential to transform NHS patient care, speeding up diagnosis and treatment times by ensuring that expert clinical resource is targeted where it has the greatest impact for the patient. 

By identifying areas in which technology can be used to help speed up patient pathways, clinicians have more time for providing personalised care and patient waiting lists – exacerbated by the pandemic, are reduced.

We have been pleased to work alongside some of the country’s leading tech minds, expert stroke clinicians, and policy leaders to develop this unique framework, which will go a long way to enabling more rapid uptake of Stroke AI software across the NHS.”

While AI can be a powerful tool in medicine, it can be difficult to ensure solutions are evidence-based and cost-effective. That’s where the new ‘Provision of AI Software in Neuroscience for Stroke Decision Making Support’ procurement framework comes in.

The framework was developed with contributions from across NHS England and NHS Improvement (NHSEI), clinical leads from the 20 Integrated Stroke Delivery Networks across England, the Academic Health Science Network, and with further input from NHSX and the Care Quality Commission.

Darrien Bold, National Digital and AI Lead for Stroke at NHSEI, commented:

“We are already seeing the impact AI decision-support software is having on stroke pathways across the country, and the introduction of this framework will drive forward further progress in delivering best-practice care where rapid assessment and treatment are of the essence.

Over the past 18 months, the heath and care system has been compelled to look to new technologies to continue providing frontline care, and the stroke community has embraced new ways of working in times of unprecedented pressure.

This framework agreement will be of great benefit as we implement the NOSIP – driving better outcomes, better patient experience and better patient safety, using new technology quickly, safely and innovatively.”

Time is very much of the essence when it comes to strokes. The framework will enable the procurement of AI solutions that analyse images to detect ischaemic or haemorrhagic strokes and provide real-time interpretations to augment the review, diagnosis, and delivery of time-dependent treatments.

While manual review of imagery can take up to 30 minutes to interpret, AI is able to do so within seconds.

“Rapid brain imaging and its interpretation is arguably one of the most important steps in the care of patients with stroke-like symptoms,” commented Dr David Hargroves, Getting It Right First Time (GIRFT) Clinical Lead for Stroke and National Specialty Advisor for Stroke Medicine at NHSEI.

“Incorporating AI decision support software is likely to improve access to disability-saving interventions to thousands of patients. This framework agreement supplies a valuable platform to support providers of hyperacute stroke care in the purchase of AI software.”

As part of the NHS Long Term Plan, the health service aims to achieve a tenfold increase in the proportion of stroke victims who receive a thrombectomy by 2022—estimated to enable around 1,600 more patients per year to live independently.

AI will be key to achieving the NHS’ long-term goals across care for stroke patients and more. We look forward to seeing all the ways health services around the world put AI to good use over the coming years to improve patient outcomes.

(Photo by Ian Taylor on Unsplash)

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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 James 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.

Interested in hearing industry leaders discuss subjects like this and their use cases? 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, Blockchain Expo, and Cyber Security & Cloud Expo.

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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.”

 Interested in hearing industry leaders discuss subjects like this and their use cases? 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, Blockchain Expo, and Cyber Security & Cloud Expo.

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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.

 Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? 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, Blockchain Expo and Cyber Security & Cloud Expo so you can explore the future of enterprise technology in one place.

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