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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During the discussion, Grande provided insightful perspectives on the current challenges, how Edge Impulse is helping address these struggles, and the tremendous promise of on-device AI.

Key hurdles with edge AI adoption

Grande highlighted three primary pain points companies face when attempting to productise edge machine learning models, including difficulties determining optimal data collection strategies, scarce AI expertise, and cross-disciplinary communication barriers between hardware, firmware, and data science teams.

“A lot of the companies building edge devices are not very familiar with machine learning,” says Grande. “Bringing those two worlds together is the third challenge, really, around having teams communicate with each other and being able to share knowledge and work towards the same goals.”

Strategies for lean and efficient models

When asked how to optimise for edge environments, Grande emphasised first minimising required sensor data.

“We are seeing a lot of companies struggle with the dataset. What data is enough, what data should they collect, what data from which sensors should they collect the data from. And that’s a big struggle,” explains Grande.

Selecting efficient neural network architectures helps, as does compression techniques like quantisation to reduce precision without substantially impacting accuracy. Always balance sensor and hardware constraints against functionality, connectivity needs, and software requirements.

Edge Impulse aims to enable engineers to validate and verify models themselves pre-deployment using common ML evaluation metrics, ensuring reliability while accelerating time-to-value. The end-to-end development platform seamlessly integrates with all major cloud and ML platforms.

Transformative potential of on-device intelligence

Grande highlighted innovative products already leveraging edge intelligence to provide personalised health insights without reliance on the cloud, such as sleep tracking with Oura Ring.

“It’s sold over a billion pieces, and it’s something that everybody can experience and everybody can get a sense of really the power of edge AI,” explains Grande.

Other exciting opportunities exist around preventative industrial maintenance via anomaly detection on production lines.

Ultimately, Grande sees massive potential for on-device AI to greatly enhance utility and usability in daily life. Rather than just raw data, edge devices can interpret sensor inputs to provide actionable suggestions and responsive experiences not previously possible—heralding more useful technology and improved quality of life.

Unlocking the potential of AI on edge devices hinges on overcoming current obstacles inhibiting adoption. Grande and other leading experts provided deep insights at this year’s AI & Big Data Expo on how to break down the barriers and unleash the full possibilities of edge AI.

“I’d love to see a world where the devices that we were dealing with were actually more useful to us,” concludes Grande.

Watch our full interview with Alessandro Grande below:

(Photo by Niranjan _ Photographs on Unsplash)

See also: AI & Big Data Expo: Demystifying AI and seeing past the hype

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This collaboration will capitalise on Absci’s Integrated Drug Creation platform—seamlessly integrating with AstraZeneca’s expertise in oncology, aiming to expedite the discovery of a potentially game-changing cancer therapy.

Under the agreement, Absci will deploy its pioneering generative AI technology to craft a therapeutic candidate antibody tailored for a specific oncology target. The collaboration encompasses an upfront commitment, substantial R&D funding, milestone payments, and royalties on future product sales.

Sean McClain, Founder & CEO of Absci, said: “AstraZeneca is a leader in developing novel treatments in oncology, and we are excited to collaborate with them to design a therapeutic candidate antibody with the potential to improve the lives of cancer patients.”

Absci’s Integrated Drug Creation platform combines generative AI and scalable wet-lab technologies, generating proprietary data by scrutinising millions of protein-protein interactions. This data fuels Absci’s proprietary AI models, facilitating the design of antibodies that are later validated through wet-lab experiments.

This accelerated approach, completing the entire cycle within approximately six weeks, enhances the probability of successful development outcomes for biologic drug candidates.

Puja Sapra, PhD, SVP of Biologics Engineering & Oncology Targeted Delivery at AstraZeneca, commented: “This collaboration is an exciting opportunity to utilise Absci’s de novo AI antibody creation platform to design a potential new antibody therapy in oncology.”

The announcement follows Absci’s recent publication on the design and validation of de novo antibodies using their state-of-the-art ‘zero-shot’ generative AI model.

The collaboration between Absci and AstraZeneca should further help to demonstrate how AI can be used to revolutionise drug discovery.

(Photo by National Cancer Institute on Unsplash)

See also: AI & Big Data Expo: AI’s impact on decision-making in marketing

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The announcement, made by Technology Secretary Michelle Donelan, marks a major step forward in harnessing the potential of AI in revolutionising healthcare. The investment will empower 22 winning projects across universities and NHS trusts, from Edinburgh to Surrey, to drive innovation and transform patient care.

Dr Antonio Espingardeiro, IEEE member and software and robotics expert, comments:

“As it becomes more sophisticated, AI can efficiently conduct tasks traditionally undertaken by humans. The potential for the technology within the medical field is huge—it can analyse vast quantities of information and, when coupled with machine learning, search through records and infer patterns or anomalies in data, that would otherwise take decades for humans to analyse.

We are just starting to see the beginning of a new era where machine learning could bring substantial value and transform the traditional role of the doctor. The true capabilities of this technology as an aide to the healthcare sector are yet to be fully realised. In the future, we may even be able to solve of some of the biggest challenges and issues of our time.

One of the standout projects receiving funding is the University College London’s Centre for Interventional and Surgical Sciences. With a grant exceeding £500,000, researchers aim to develop a semi-autonomous surgical robotics platform designed to enhance the removal of brain tumours. This pioneering technology promises to elevate surgical outcomes, minimise complications, and expedite patient recovery times.

“With the increased adoption of AI and robotics, we will soon be able to deliver the scalability that the healthcare sector needs and establish more proactive care delivery,” added Espingardeiro.

University of Sheffield’s project, backed by £463,000, is focused on a crucial aspect of healthcare – chronic nerve pain. Their innovative approach aims to widen and improve treatments for this condition, which affects one in ten adults over 30.

The University of Oxford’s project, bolstered by £640,000, seeks to expedite research into a foundational AI model for clinical risk prediction. By analysing an individual’s existing health conditions, this AI model could accurately forecast the likelihood of future health problems and revolutionise early intervention strategies.

Meanwhile, Heriot-Watt University in Edinburgh has secured £644,000 to develop a groundbreaking system that offers real-time feedback to trainee surgeons practising laparoscopy procedures, also known as keyhole surgeries. This technology promises to enhance the proficiency of aspiring surgeons and elevate the overall quality of healthcare.

Finally, the University of Surrey’s project – backed by £456,000 – will collaborate closely with radiologists to develop AI capable of enhancing mammogram analysis. By streamlining and improving this critical diagnostic process, AI could contribute to earlier cancer detection.

Ayesha Iqbal, IEEE senior member and engineering trainer at the Advanced Manufacturing Training Centre, said:

“The emergence of AI in healthcare has completely reshaped the way we diagnose, treat, and monitor patients.

Applications of AI in healthcare include finding new links between genetic codes, performing robot-assisted surgeries, improving medical imaging methods, automating administrative tasks, personalising treatment options, producing more accurate diagnoses and treatment plans, enhancing preventive care and quality of life, predicting and tracking the spread of infectious diseases, and helping combat epidemics and pandemics.”

With the UK healthcare sector already witnessing AI applications in improving stroke diagnosis, heart attack risk assessment, and more, the £13 million investment is poised to further accelerate transformative healthcare breakthroughs.

Health and Social Care Secretary Steve Barclay commented:

“AI can help the NHS improve outcomes for patients, with breakthroughs leading to earlier diagnosis, more effective treatments, and faster recovery. It’s already being used in the NHS in a number of areas, from improving diagnosis and treatment for stroke patients to identifying those most at risk of a heart attack.

This funding is yet another boost to help the UK lead the way in healthcare research. It comes on top of the £21 million we recently announced for trusts to roll out the latest AI diagnostic tools and £123 million invested in 86 promising tech through our AI in Health and Care Awards.”

However, the announcement was made the same week as NHS waiting lists hit a record high. Prime Minister Rishi Sunak made reducing waiting lists one of his five key priorities for 2023 on which to hold him “to account directly for whether it is delivered.” Hope is being pinned on technologies like AI to help tackle waiting lists.

This pivotal move is accompanied by the nation’s preparations to host the world’s first major international summit on AI safety, underscoring its commitment to responsible AI development.

Scheduled for later this year, the AI safety summit will provide a platform for international stakeholders to collaboratively address AI’s risks and opportunities.

As Europe’s AI leader, and the third-ranking globally behind the USA and China, the UK is well-positioned to lead these discussions and champion the responsible advancement of AI technology.

(Photo by National Cancer Institute on Unsplash)

See also: BSI publishes guidance to boost trust in AI for healthcare

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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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Brits have become used to long NHS wait times for many years. However, the post-covid backlog has sent the number of patients on waiting lists rocketing:

There are many strong views on what NHS reforms are needed, but one thing everyone can agree on is that the current trajectory is unsustainable. Modern technologies will be vital in delivering the improvements that will help both NHS staff and patients.

The Walton Centre NHS Foundation Trust has announced a partnership with TCS to develop digital solutions that increase the productivity of specialists, reduce waiting times for patients, and improve the overall experience. 

Shalini Mathur, Business Unit Head of Public Services for the UK, Europe, and ANZ at TCS, said:

“We are pleased to partner with The Walton Centre to transform patient care in the UK using next-gen technologies.

These technologies and solutions will help reduce waiting times for patients while improving the productivity of specialist consultants. This creates a blueprint for similar digital innovation in other clinical settings.”

The first product of this partnership is an AI chatbot that aims to transform how patients with headaches are diagnosed and treated.

A headache can range from nothing serious to being potentially fatal or life-changing. Anyone who is concerned their headache could be out-of-the-ordinary should get it checked out, but deciding which patients should be prioritised is a critical but difficult task.

Patients with headaches make up the largest number of outpatient referrals to neurologists at The Walton Centre. The use of a chatbot will enable information about the patient’s condition to be collected in order to compile a detailed report for clinicians to review before an initial appointment.

Dependent on the clinician’s assessment, a patient may be put on a fast track for an examination or offered guidance on alleviating symptoms while they wait for their turn.

Dr Anita Krishnan, Divisional Clinical Director for Neurology at The Walton Centre, and a Consultant Neurologist specialising in headaches, commented:

“Technology is a huge part of medicine and it’s exciting to work with TCS to create a new artificial intelligence-based solution which will help our patients.

The chatbot system also has the potential to be extended into other areas of medicine, which could benefit even more patients.

We are working closely with TCS and our other specialist partners to ensure the new solution is effective and safe and improves efficiency and patient outcomes.”

While it’s a fairly limited trial to begin with, AI-powered chatbots could make a real difference across the NHS. Chatbots can help to ensure that patients are correctly prioritised and less of the scarce time consultants have available is spent having to ask the questions for information that can be collected beforehand.

Chatbots aren’t going to solve all of the NHS’ problems, but they should make a positive difference for staff and patients.

(Photo by Nicolas J Leclercq on Unsplash)

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Nuance became a household name for creating the speech engine recognition engine behind Siri. In recent years, the company has put a strong focus on AI solutions for healthcare and is now a full-service partner of 77 percent of US hospitals and is trusted by over 500,000 physicians daily.

Earlier this year, Microsoft acquired Nuance with the promise of ushering in a “new era of outcomes-based AI”. Microsoft is also active in the healthcare space and its acquisition of Nuance was investigated by regulators over concerns it may reduce competition.

Regulators ultimately ended up giving the deal the thumbs up.

The EU’s regulator, for example, concluded that Nuance would continue to face stiff competition in the future, the data Microsoft gains would not provide it with an advantage to shut out competitors, and there’d be no ability/incentive to foreclose existing solutions.

“Combining the power of Nuance’s deep vertical expertise and proven business outcomes across healthcare, financial services, retail, telecommunications, and other industries with Microsoft’s global cloud ecosystems will enable us to accelerate our innovation and deploy our solutions more quickly, more seamlessly, and at greater scale to solve our customers’ most pressing challenges,” said Mark Benjamin, CEO of Nuance, at the time of Microsoft’s acquisition.

Nuance says both companies represent two of the most trusted and innovative technology organisations in the world. As such, it believes Nuance and Microsoft are in a position to foster a community anchored in collaboration with key leading health systems (LHS) executives and experts across the healthcare ecosystem. That community will be The AI Collaborative.

“Our members have expressed their desire for a dedicated space to explore AI in healthcare and its enormous potential to improve outcomes and clinical workflow,” said Renee DeSilva, CEO of The Academy.

“We are thrilled to expand our partnership with Microsoft and Nuance to introduce The AI Collaborative, a new program at The Academy designed exclusively for clinical and operational executives who lead their organization’s approach to investing in AI as a strategic initiative.”

The AI Collaborative will bring together senior leaders from LHS to understand their current and future needs and create the AI and ML innovations required to fulfil them.

“The key to successful healthcare innovation using AI is understanding at a deep level the problems that you’re trying to solve and focusing on the outcomes you want to achieve,” explained Peter Durlach, Chief Strategy Officer of Nuance.

“With the combined engineering, market and domain expertise of Nuance and Microsoft, The AI Collaborative can bring together multiple technical, business and clinical stakeholders to prioritize deployment of solutions for clinician burnout, patient engagement and health system financial stability, while accelerating innovation in precision medicine, drug discovery, clinical decision support and other promising use cases across the entire healthcare ecosystem.”

The AI Collaborative will commence in September 2022 and kick off with a visit to Microsoft’s corporate HQ. Annual summits will be held going forward where stakeholders will learn how to best utilise patient-specific data and insights to augment care delivery, reduce care variation, and support operational improvements.

(Photo by Cytonn Photography on Unsplash)

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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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London-based Babylon Health is a digital-first health service provider that uses AI and machine learning technology to provide access to health information to people whenever and wherever they need it.

The company has partnered with private and public across the UK, North America, South-East Asia, and Rwanda with the aim of making healthcare more accessible and affordable to 24 million patients worldwide.

“Our job is to help people to stay well and we’re on a mission to provide affordable, accessible health care to everyone in the world,” explains Richard Noble, Engineering Director of Data at Babylon.

Babylon Health’s rapid growth has led it to seek a partner to help it scale.

By partnering with Google Cloud, the company claims that it’s been able to:

• Increase event data ingestion from 1 TB per week to 190 TB daily
• Reduce the wait time for users to access data from six months to a week
• Integrate over 100 data sources – providing access to 80 billion data points
• Save hundreds of hours of work by automatically transcribing 100,000 video consultations in 2021

Babylon Health needs to store and process huge amounts of sensitive data.

“We work with a lot of private patient data and we must ensure that it stays private,” explains Natalie Godec, cloud engineer at Babylon. “At the same time, we must enable our teams to innovate with that data while meeting different national regulatory standards.”

Therefore, Babylon Health required a partner it felt could handle such demands.

“We chose Google Cloud because we knew it could scale with us and support us with our data science and analysis and we could build the tools we needed with it quickly,” added Noble. “It offers the solutions that enable us to focus on our core business, access to health.”

Babylon Health says the move to Google Cloud has enabled it to better analyse its data using AI to unlock new tools and features that help clinicians and users alike. While building a new data model and giving access to users initially took six months, the company says it now takes under a week.

In London, Babylon Health offers its ‘GP at Hand’ service which – in partnership with the NHS – acts as a digital GP practice. Patients can connect to NHS clinicians remotely 24/7 and even be issued prescriptions if required. Where physical examinations are needed, patients will be directed to a suitable venue.

However, GP at Hand has been criticised as “cherry-picking” healthier patients—taking resources away from local GP practices that are often trying to care for sicker, more elderly patients.

Growing pains

While initial problems are to be expected from any relatively new service; poor advice in a healthcare service could result in unnecessary suffering, long-term complications, or even death.

In 2018, Dr David Watkins – a consultant oncologist at Royal Marsden Hospital – reached out to AI News to alert us to Babylon Health’s chatbot giving unsafe advice.

Dr Watkins provided numerous examples of clearly dangerous advice being given by the chatbot:

Babylon Health called Dr Watkins a “troll” who has “targeted members of our staff, partners, clients, regulators and journalists and tweeted defamatory content about us”.

According to Babylon Health, Dr Watkins 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.

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”. He estimates conducting between 800 and 900 full triages and that some were repeat tests to see whether Babylon Health had fixed the issues he previously highlighted.

That same year, Babylon Health published a paper claiming that its AI could diagnose common diseases as well as human physicians. The Royal College of General Practitioners, the British Medical Association, Fraser and Wong, and the Royal College of Physicians all issued statements disputing the paper’s claims.

Dr Watkins has acknowledged that Babylon Health’s chatbot has improved and has substantially reduced its error rate. In 2018, when Dr Watkins first reached out to us, he says this rate was “one in one”.

In 2020, Babylon Health claimed in a paper that it can now appropriately triage patients in 85 percent of cases.

Hopefully, the partnership with Google Cloud continues to improve Babylon Health’s abilities to help it achieve its potentially groundbreaking aim to deliver 24/7 access to healthcare wherever a patient is.

(Photo by Hush Naidoo Jade Photography on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

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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)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

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