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.

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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.

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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.”

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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.

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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.

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AI has many benefits over conventional analytics and clinical decision-making methods. As they interact with training data, learning algorithms may become more exact and accurate, enabling patients to acquire new insights into diagnoses, care procedures, treatment variability, and outcomes.

1. Artificial Intelligence Integrated Into Major Disease Areas Enables Predictive Analytics for Early Intervention 

With cardiovascular, neurological, and cancer illnesses continuing to be the major causes of death, it is essential to use all available resources to aid in early detection, diagnosis, and treatment. Thanks to artificial intelligence, it’s possible to identify any potential danger signs in a patient’s behavior early on. For example, patients with a high risk of stroke were identified using AI algorithms based on their reported symptoms and genetic characteristics; this level was movement-based, with every atypical physical movement in the patient being recorded and generating an alert.

This trigger warning enabled practitioners to expeditiously refer patients for an MRI/CT scan for disease assessment. The research found that the early detection alert had an accuracy of more than 85 percent in evaluating diagnosis and prognosis. Consequently, practitioners were able to initiate therapy more quickly and identify whether a patient faced a greater risk of future stroke. Similarly, machine learning was used to predict if a patient will have another stroke 48 hours later with a perdition accuracy of 70%.

Cancer is a multifaceted and complicated disease characterized by hundreds of genetic and epigenetic variants. AI-based algorithms have the potential to pave the way for the early detection of these genetic alterations and abnormal protein interactions. Biomedical research in the modern era is also focused on bringing AI technology to clinics in a safe and ethical manner. AI-assisted pathologists and doctors may represent a quantum leap ahead in disease risk, diagnosis, prognosis, and therapy prediction. Clinical applications of artificial intelligence and machine learning in cancer diagnosis and therapy are the future of medical guidance, paving the way for more rapid mapping of a new treatment for each person. Researchers may interact in real-time and exchange information digitally by utilizing an AI-based system method, which has the potential to treat millions of patients. Science is concentrating on demonstrating game-changing technologies of the future in clinics by bridging the gap between biology and artificial intelligence, and explaining how AI-based support may aid oncologists in providing accurate therapy.

2. Artificial Intelligence and Machine Learning Can Provide More Targeted Diagnostics

With a vast volume of healthcare data out in the field, AI must effectively sift through it in order to “learn” and create a network. There are two kinds of data that can be sorted in the domain of healthcare data: unstructured and structured. Structured learning employs three techniques: Machine Learning Techniques (ML), a Neural Network system, and Modern Deep Learning. Natural Language Processing is used in any unstructured data (NLP).

The use of analytical algorithms in order to extract particular patient characteristics, which include all of the information that would be gathered during a patient visit with a practitioner, is the basis of machine learning methods. Symptoms, results of physical exams, medications, basic metrics, disease-specific data, diagnostic imaging, gene expressions, and a variety of laboratory tests are all included in the structured data that is collected. Patient outcomes may then be predicted using machine learning. In one research, Neural Networking was used to select 6,567 genes and match them with texture information from the patients’ mammograms in a breast cancer diagnosis procedure. This combination of both genetic and morphological features resulted in a tumor indication that was more specific.

Supervised learning is the most frequent form of Machine Learning in a healthcare context. Supervised learning makes use of the patient’s physical characteristics in conjunction with a database of information to give a more focused result. Modern Deep Learning is another kind of learning that is utilized and is thought to go beyond the surface of Machine Learning. When compared to Machine Learning, Deep Learning utilizes the same inputs, but it feeds them into a computerized neural network, which is a hidden layer that further processes the information in order to give a more simple output. This assists practitioner in narrowing down many potential diagnoses to one or two outcomes, enabling the practitioner to reach a more definite and concrete decision.

3. Artificial Intelligence Has the Potential To Provide the Next Generation of Radiological Tools

Based on the incidence, severity, and preventability of the illnesses, occupational lung diseases are the most common cause of occupation-associated disease in the United States. Exposure to organic and inorganic compounds as well as carcinogens in the workplace, over a lengthy period of time, may result in a variety of lung illnesses that can have long-term consequences even after the exposure ends.

Each year, new causes of respiratory damage emerge, leading to an increase in the number of workers who develop lung illness as a result of their jobs. During the early years of work, the majority of individuals who are frequently exposed to toxins on the job show relatively minor signs of lung problems. Lung cancer and other respiratory diseases have a long incubation period, which makes it difficult to establish a connection between occupational exposure and these illnesses for many years.

Due to the limits of human vision, up to 35% of lung nodules go unnoticed during the first checkup. Artificial intelligence can help in both cases by relieving physicians of some of their responsibilities and by detecting lung spots that aren’t visible to the naked eye. According to a recent study published in the JAMA Network Open, an artificial intelligence system taught to identify pulmonary nodules may enhance lung cancer diagnosis on chest radiographs.

By using artificial intelligence as a second reader in conjunction with chest X-rays, radiology trainees and board-certified radiologists may enhance their performance in suggesting chest CT scans for patients suspected of having lung cancer. According to the researchers’ findings, the AI algorithm helps less-experienced readers in terms of sensitivity while benefiting more-experienced readers in terms of specificity.

Magnetic resonance imaging (MRI), computed tomography (CT), and X-rays are examples of current medical imaging technologies that offer non-invasive views into the workings of the human body. However, many diagnostic procedures still depend on actual tissue samples acquired via biopsies, which are associated with hazards such as the likelihood of infection in the patient. Experts anticipate that artificial intelligence will allow the development of next-generation radiological tools that are accurate and comprehensive enough to obviate the need for tissue samples in some cases.

If scientists want the imaging to provide the same information as tissue samples, they’ll need to be able to obtain extremely tight registration so that the high accuracy for each pixel can be ascertained. Successful completion of this quest may enable physicians to get a more accurate knowledge of how tumors behave as a whole, rather than relying on the characteristics of a particular section of the malignancy to make treatment choices. Additionally, providers may be able to more accurately identify the aggressiveness of tumors and focus therapies accordingly. Artificial intelligence is assisting in the advancement of “virtual biopsies” and the cutting-edge science of radionics, which focuses on using image-based algorithms to describe the phenotypic and genetic characteristics of tumors.

4. Telehealth, the Artificial Intelligence on a Smaller Scale, Can Lower Healthcare Costs, Drive Up Efficiency and Provide Patients Better Access to Healthcare Services

COVID-19 generated an urgent need for telemedicine to care for patients outside of the clinic or office environment, as well as to mitigate financial losses due to decreased ambulatory visits. According to new data, the number of patients utilizing telehealth rose from 11 percent in 2010 to 46 percent in 2020, with growth expected to continue. Telehealth may account for 20%, or $250 billion, of US healthcare expenditure in the near future.

Although artificial intelligence is utilized on a wider scale for high-risk illnesses, telehealth tools are being deployed in patients’ homes to help treat and prevent high-risk scenarios while simultaneously decreasing hospital readmissions. Telehealth technologies enable various parameters to be collected, recorded, and analyzed in the same way as a larger AI system would. This tool may immediately notify practitioners if a patient reports a high-risk trait. Fast diagnoses and an updated treatment plan save time and money for both the patient and the hospital while delivering more rapid care. Artificial intelligence allows practitioners to make more efficient and rational decisions, thus enhancing patient care. 

Respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, occupational lung diseases, and pulmonary hypertension are among the most common, underdiagnosed, disabling, fatal, and expensive to treat of the many chronic diseases. Connected respiratory care and telehealth provide access to services that were previously only available at medical facilities. They allow for effective and continuous monitoring, early intervention, and multidisciplinary team care, which may be especially important for patients with advanced disease, multiple comorbidities, or frequent exacerbations – in other words, patients who require more intensive management that addresses the complexities of their disease. Connected care also promises enhanced patient access, more efficient and effective use of health resources, a better patient experience, and reduced medical expenses.

References:

https://svn.bmj.com/content/2/4/230

https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality

https://svn.bmj.com/content/svnbmj/2/4/230.full.pdf

https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2770952

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Racial inequality continues to be rampant in healthcare. Examining the fallout of COVID-19 serves as yet another example of the disparity between ethnicities.

In England and Wales, males of Black African ethnic background had the highest rate of death involving COVID-19, 2.7 times higher than males of a White ethnic background. Females of Black Caribbean ethnic background had the highest rate, 2.0 times higher than females of White ethnic background. All ethnic minority groups other than Chinese had a higher rate than the White ethnic population for both males and females.

Such disparities are sadly common across many conditions that can reduce life enjoyment, limit opportunities, and even lead to premature death. AI could be a powerful aid in tackling the problem, if thoroughly tested and implemented responsibly.

“As the first health and social care secretary from an ethnic minority background, I care deeply about tackling the disparities which exist within the healthcare system,” explained Javid, speaking to The Guardian.

Among the projects given the green light by Javid include the creation of new standards for health inclusivity to improve the representation of ethnic minorities in datasets used by the NHS.

“If we only train our AI using mostly data from white patients it cannot help our population as a whole,” added Javid. “We need to make sure the data we collect is representative of our nation.”

A recent analysis found a significant disparity in performance when using computer screening to detect diabetic retinopathy in patients from ethnic minority communities due to different levels of retinal pigmentation. One project will attempt to address this disparity.

Among the devastating statistics affecting minority communities is that black women are five times more likely to die from complications during pregnancy than white women. One project will use algorithms to investigate the factors and recommend changes – including potentially new training for nurses and midwives – that will hopefully ensure that everyone has the best possible chance to live a healthy life with their child.

The development of an AI-powered chatbot also hopes to raise the uptake of screening for STIs/HIV among minority ethnic communities.

The drive will be led by NHSX. A report in 2017 by PwC found that just 39 percent of the UK public would be willing to engage with AI for healthcare. However, research (PDF) by KPMG found that – despite an overall unwillingness from the British public to share their data with the country’s biggest organisations even if it improved service – the NHS came out on top with 56 percent willing to do so.

If the UK Government wants to use AI as part of its “level up” plans, it will need to tread carefully with a sceptical public and prove its benefits while avoiding the kind of devastating missteps that have cost thousands of lives and defined Johnson’s premiership so far.

(Image Credit: UK Parliament under CC BY 3.0 license)

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

The post UK health secretary hopes AI projects can tackle racial inequality appeared first on AI News.

Google-owned DeepMind was quietly given the personal records of 1.6 million patients at the Royal Free London NHS Foundation Trust.

DeepMind said that it was using the data to create a potentially life-saving app called Streams. The app was designed to alert, diagnose, and detect when patients were at risk of developing acute kidney injury. It’s currently in the process of being decommissioned.

Several investigations were launched, including by the Information Commission that said in 2017 the hospital had not done enough to protect the privacy of patients when it shared data with Google.

Following that ruling, DeepMind apologised and said that it should have been thinking about the needs of patients rather than on building tools for clinicians.

The new case is being handled by law firm Mishcon de Reya on behalf of the lead plaintiff Andrew Prismall and the over 1.5 million other affected patients.

Mr Prismall said: “Given the very positive experience of the NHS that I have always had during my various treatments, I was greatly concerned to find that a tech giant had ended up with my confidential medical records.

“As a patient having any sort of medical treatment, the last thing you would expect is your private medical records to be in the hands of one of the world’s biggest technology companies.

“I hope that this case will help achieve a fair outcome and closure for all of the patients whose confidential records were obtained in this instance without their knowledge or consent.”

In the UK, such cases are “opt-out” which means all impacted parties will be included in the action unless they specifically request not to be. Given the size of the action, the overall payout for DeepMind could be large but small for each individual.

The case is just one of a growing number of high-profile cases around data collection in recent years. In April, Anne Longfield, the former Children’s Commissioner for England, filed a case against TikTok on behalf of millions of UK children over how the app collected and used their data.

Data collection is vital for training AIs that could very well save lives. However, individual privacy is also important.

Mishcon Partner Ben Lasserson, who is leading the DeepMind case, said: “This important claim should help to answer fundamental questions about the handling of sensitive personal data and special category data.

“It comes at a time of heightened public interest and understandable concern over who has access to people’s personal data and medical records and how this access is managed.”

As the UK leaves the EU it is looking to change its data laws. While the EU’s laws have often been criticised as being too strict and leaving innovation to happen outside of Europe, there are concerns that the UK could become too relaxed despite ministers’ pledges not to.

“There’s an opportunity for us to set world-leading, gold standard data regulation which protects privacy, but does so in as light-touch a way as possible,” said Culture Secretary Oliver Dowden.

Britain’s data watchdog is also getting a shakeup and will be led by John Edwards, New Zealand’s current privacy commissioner.

“There is a great opportunity to build on the wonderful work already done and I look forward to the challenge of steering the organisation and the British economy into a position of international leadership in the safe and trusted use of data for the benefit of all,” commented Edwards.

Only time will tell if the UK manages to strike a balance between the strictness of the EU and the somewhat laissez-faire approach taken by many of the world’s powers. However, the cases against DeepMind and others show that a modernisation of laws and thinking is needed to unlock the potential of AI to improve/save lives while protecting individual privacy.

(Photo by Tugce Gungormezler on Unsplash)

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

The post DeepMind hit with class-action lawsuit over NHS health data scandal appeared first on AI News.