In the hands of a trained specialist, AI may be a game-changer for cardiovascular diseases (CVDs). From predicting heart attacks to enhancing clinical decision-making, learn how AI is reshaping healthcare.

According to the World Health Organization (WHO),¹ ‘Prioritising AI for health is crucial.’ What does this mean in practice? Harnessing the vast potential of AI and machine learning, while avoiding their pitfalls, may be a game-changer for CVDs. 

Earlier diagnosis of cardiovascular disease

To help predict the risk of a patient developing CVD, the global leading cause of disease and mortality,² doctors combine their expertise with a variety of tools and algorithms. Recently, a machine-learning CVD risk prediction model was found to have an accuracy of up to 98.4%, outperforming existing tools.³ Such applications of AI could potentially have significant implications for reducing CVD-related morbidity and mortality. 

The applications of AI in CVD
diagnosis are promising, with
potential to improve patient outcomes.

AI-assisted interpretation of ECGs and echocardiograms

Echocardiography, cardiac CT and coronary CT angiography (CTCA) — all types of heart scans — are essential tools for diagnosing coronary artery disease (CAD), a form of CVD and a condition that causes heart attacks.

Numerous studies have shown the promise of machine learning in detecting CAD on CTCA and cardiac CT. In one study, a machine learning model accurately interpreted CAD risk based on a finding on cardiac CT scans within six seconds, with similar accuracy to expert interpretation taking 15 minutes.

Interpretation of echocardiography can be subjective, and some signs of CAD are subtle. An AI model recently assessed stress echocardiogram images and could accurately identify patients with severe CAD, helping to inform treatment.  

Potential advantages and pitfalls

The applications of AI in CVD diagnosis are promising, with potential to improve patient outcomes; for example, by aiding in detecting, classifying and providing prognosis.⁶ However, some AI models can be susceptible to generating erroneous information or connections,⁷ a phenomenon known as a ‘hallucination.’ It is sometimes uncertain how AI arrives at its conclusions.⁶

Clinicians and healthcare providers who use AI need to be mindful of the potential risks. The WHO emphasised that the use of AI technologies in healthcare should be managed by suitably trained people and that there should be accountability for their use.¹ 

Rather than relying solely on AI, a collaborative approach where clinicians incorporate AI data and recommendations into their decision-making is likely to deliver better outcomes. This was evident in the study on stress echo interpretation, where AI-assisted image interpretation increased clinicians’ performance and confidence in decision-making.⁵ 

Looking ahead, the use of AI in CVD diagnosis and other diseases is likely to advance rapidly, guided by further research to maximise benefits while ensuring safety.  

[1] WHO, 2021. WHO issues first global report on artificial intelligence (AI) in health and six guiding principles for its design and use. Available:WHO issues first global report on Artificial Intelligence (AI) in health and six guiding principles for its design and use 
[2] Sapra V et al, 2023. Integrated Approach using deep neural network and CBR for detecting severity of coronary artery disease. Alexandria Engineering Journal. 68 (1) – 709-720. Available: https://www.sciencedirect.com/science/article/pii/S1110016823000443 
[3] Fitriyani N L et al, 2020. HDPM: An effective heart disease prediction model for a clinical decision support system. IEEE Access. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9144587 
[4] Upton R et al, 2022. Automated echocardiographic detection of severe coronary artery disease using artificial intelligence.  JACC Cardiovascular Imaging. 15(5): 715-727. https://www.sciencedirect.com/science/article/pii/S1936878X21007804?via%3Dihub 
[5] Al-Zaiti S et al, 2023. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nature Medicine. Available: https://www.nature.com/articles/s41591-023-02396-3.pdf  
[6] Barry J et al, 2023. The role of artificial intelligence in echocardiography. Journal of Imaging, 9(2), 50. Available: J. Imaging | Free Full-Text | The Role of Artificial Intelligence in Echocardiography (mdpi.com)
[7] Schwartz I S et al, 2024. Black Box Warning: Large language models and the future of infectious diseases consultation. Clinical Infectious Diseases, 78(4): 860-866. Available: Black Box Warning: Large Language Models and the Future of Infectious Diseases Consultation | Clinical Infectious Diseases | Oxford Academic (oup.com) 

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Clinical research is becoming more patient-centric, but there’s still more that needs to be done to make clinical trials available to everyone. This could include wider participation.

Clinical research is increasingly being recognised as a viable care option, improving health outcomes while accelerating clinical research. Yet, as few as 3% of eligible patients sign up. 

How to increase patient participation

Pharmaceutical companies are working on overturning this statistic by designing and building trials that centre around participant needs. This includes greater patient involvement in trial design; more opportunities for treatment at home; fewer tests and assessments; and new technologies for support and monitoring.  

However, it seems clinical trial participation is not for everyone. Could it be because a huge amount of trust, awareness and understanding is needed to become a clinical trial participant?  

Where the ‘activated’ patient comes in 

According to health researcher Judith Hibbard, activated patients, who are actively engaged in managing their health and advocating for themselves, are better equipped to handle health conditions and contribute to treatment decisions. 

Although, it’s not always an equal starting point because not everyone will be at activated status. Because initiatives often engage with those who are already activated and have high levels of health literacy, the complexity of clinical trial information means that some patients defer decision-making to their doctors. It may not be on purpose, but this excludes many from potentially life-changing medicines.  

The complexity of clinical trial
information means that some patients
defer decision-making to their doctors.

Empower patients through clinical trials

This may be an opportunity for those in charge of clinical trials to take a leading role in helping patients to become activated, creating a push-pull that works in all directions. Consider clinical trial education and awareness resources developed with everyone in mind, helping to bridge the knowledge gap and supporting people to make informed decisions for their own health.

Shift towards community-inclusive trials

The industry is already beginning to shift towards more patient-centred clinical trial recruitment and providing trial information that speaks to all — but that’s just the start. Perhaps it’s time to bring whole communities into contact with the clinical research world instead, giving everyone an opportunity to share their opinions and ideas — a chance to feel part of something bigger. It might not be easy, but it will be worth it.  

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Dr Rosa Montero

Renal & Transplant Consultant, St George’s University Hospitals NHS Foundation Trust

It is difficult to know what to do when you have chronic kidney disease. Find some dietary and lifestyle advice as a guide on how to have good kidney health.

Eating more plant-based foods and fewer animal proteins, additives and processed foods is associated with improved wellbeing and health. Focus on whole grains, vegetables, fruits, beans and cooking from scratch.

Healthier diet: moderate salt, limit alcohol

A daily salt intake of less than 5g (2.5g sodium) is recommended to protect against high blood pressure and heart disease. Try using the ‘traffic light’ labelling on packaged food to choose lower salt options, but avoid potassium-based salt substitutes and any food items containing potassium chloride. Alcohol is best limited to less than two units in men and one unit in women per day.

CKD caution: consult for supplements

People with chronic kidney disease (CKD) should avoid non-prescription medications, drinks and supplements unless checked with a pharmacist or doctor because these can be harmful. The exception is Vitamin D3 (cholecalciferol) as most people in the UK need Vitamin D supplements to avoid deficiency, during the winter and throughout the year for those with limited sun exposure.

People with chronic kidney disease
(CKD) should avoid non-prescription
medications, drinks and supplements.

Weight wisdom and balanced lifestyle

Achieving a healthy weight is recommended through sustainable lifestyle changes like more activity and better eating habits, such as controlling portions and limiting snacks and takeaways.

Carbohydrates, ideally wholegrain, should stay on the menu. Diets advocating high protein, fasting or any extreme approach should be avoided or only attempted with medical supervision.

Being more active

Increasing activity has proven beneficial and important to include in daily life. The UK Kidney Association recommend aiming for 150 minutes of moderate physical activity each week. Brisk walking, housework and dancing all count, but check with your doctor when starting something new.

Wellbeing support resources

Wellbeing offers enormous benefits, with mental health gaining recognition. Kidney units offer dedicated support services, including access to counsellors and psychologists, so ask for help. Online support groups and renal charities, including Kidney Care UK and Kidney Research UK, are also important sources for mental health, lifestyle and wellbeing.

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Sue Lyon

Chair, UK Kidney Association Patient Council

Learn about the complexities of kidney health in the UK. Explore disparities in ethnicity, socioeconomic status, sex and age impacting outcomes.

In the UK, kidney health disparities present a complex challenge. With over 7.2 million people grappling with chronic kidney disease, as reported by Kidney Research UK, the urgency for a collective, informed response has never been greater.

Latest data highlights kidney health disparities

A newly released ‘Disparities Report,’ utilising the latest data from the UK Renal Registry and spearheaded by kidney patients themselves, vividly illustrates the complex landscape of kidney disease. It emphasises how factors such as ethnicity, socioeconomic status, sex and age distinctly shape the risk, treatment and outcomes associated with the condition.

Ethnicity and socioeconomic factors

Ethnicity plays a significant role in the incidence and management of kidney disease. Black and other UK minority ethnic populations face higher rates of kidney failure — and at younger ages — compared to their white counterparts. This is underscored by the disproportionate impact of diabetes, particularly prominent among Asian and Black populations, as a leading cause of kidney failure.

However, the challenge doesn’t end at diagnosis. Socioeconomic factors also have an impact, with individuals in deprived areas facing not just a higher risk of kidney failure and diabetes but also reduced access to treatments that provide the greatest freedom and flexibility like home therapies and transplantation.

Males are more likely to experience
kidney failure at all stages of life.

Age and sex disparities

Sex and age disparities in kidney health further complicate the healthcare landscape. Older individuals are more likely to begin treatment for kidney failure and experience lower survival rates. Males are more likely to experience kidney failure at all stages of life. 

While adult males and females appear equally able to access transplantation, boys appear more likely to receive a transplant at the onset of their treatment than girls. Transplantation is the first line of treatment for one in five children, but disparities in access to transplantation between boys and girls suggest work is needed to ensure all children have equal access to optimal treatment.

Addressing broader health equity

Addressing these disparities requires a concerted effort from healthcare providers, policymakers and the community. It calls for a deeper understanding of the underlying causes and a commitment to developing targeted interventions that ensure equitable access to kidney care for all individuals.

This focus on kidney health disparities is a crucial step towards understanding and mitigating the broader issue of health inequalities in the UK. It challenges us to look beyond the medical aspects of kidney disease and consider the social determinants of health that contribute to these disparities, ensuring that everyone — regardless of background — has access to the care and treatment they need.

The UK Kidney Association

The UKKA is the leading professional body for the UK kidney community. Since 1950, they have been committed to making tangible, innovative improvements to the care of people living with kidney disease, achieved through the support of professionals and the kidney health system.

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Dr Matt Howard

Head of Health Data Sciences, Amazon Web Services (AWS)

Dr Francisco Azuaje

Director of Bioinformatics, Genomics England

Generative artificial intelligence (genAI) is empowering healthcare organisations to better leverage their data to improve the patient experience, address workforce challenges and boost productivity.

By handling routine backroom tasks, genAI frees clinicians from administrative duties, allowing them more time with patients by automatically generating common documents like discharge notes and GP letters. Additionally, genAI mines data from documents, including scientific literature, to aid biomedical research and improve patient outcomes with new therapies.

GenAI for healthcare admin automation

Former GP and Head of Digital Transformation at Amazon Web Services (AWS), Dr Andrew Jones, says that when it comes to improving healthcare workforce productivity, a major challenge is the amount of time clinical staff spend on administration. 

A physician with 20 years’ experience in the NHS in both primary and secondary care, he points to scenarios where following a patient consultation, GPs would type into the electronic health record, order tests and then make a referral repeating the same information each time. 

“One of the things that genAI is highly efficient at, is transcribing patient-clinician conversations,” says Jones. 

Data enhancing patient records

 The cloud provider has systems to ease this administrative burden on clinicians. “We have also developed services that take out key entities and important information from these conversations — such as medication, diagnoses and tests — summarise that and put it into the patient record,” he adds. “That is going to save doctors a huge amount of time.”

Aiding in medical document processing

As more patients seek access to their records, AWS is also being asked to design systems that provide summaries of medical documents that are accessible and understandable. Dr Matthew Howard, Head of Health Data Sciences, outlines how a range of customers — from hospitals and clinics to strategic research institutions — are assisted in getting started with these technologies. 

As well as supporting with automation of high-volume tasks and extracting information, compliance and ensuring privacy and data security is a critical element, insists Howard. He leads a team of applied data scientists who work with customers, harnessing critical data to develop generative AI-based healthcare solutions.

One of the things that genAI is highly efficient at, is transcribing patient-clinician conversations.

Projects include multimodal cancer patient stratification, application of genAI to automate literature review and development of genAI-based medical information systems. “We provide customers access to a range of genAI models, allowing them to identify the best performing, depending on use,” he adds. We are committed to developing AI responsibly.

By prioritising ethical considerations and taking advantage of the tools, support and healthcare expertise, organisations can harness the full potential of generative AI while ensuring data protection and fairness in decision-making processes. 

Assisting biocurators in knowledge curation

The company is working closely with Genomics England, a global leader in enabling genomic medicine and research, on using genAI to accelerate research. Dr Francisco Azuaje, Director of Bioinformatics at Genomics England, says they partner with the company to use genAI tools to help biomedical knowledge management experts (biocurators) process large amounts of information from scientific literature. 

He explains: “This knowledge of medical conditions from the scientific literature is needed to help clinical experts establish connections between gene variants and diseases. The knowledge curation process aims to improve and maintain the knowledge base for supporting clinical diagnosis.”

More accurate diagnosis for improved patient outcomes

The AWS services and genAI research application, he continues, enables biomedical experts to efficiently extract and prioritise clinically relevant associations between gene variants and medical conditions from numerous scientific articles, ensuring Genomics England incorporates existing and new findings into its knowledge management pipelines. 

Azuaje says: “This application is very important for us because, by integrating these findings into the Genomics England knowledge base, it provides evidence to help inform future decisions on the introduction of new genomic technologies which could make a real difference to the diagnosis and care of patients with rare conditions.”

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Automated medicine dispensing at home can improve medication adherence, minimise errors creeping into complex medication regimens and help patients live independent lives.

Mika Apell, CIO at Evondos, has witnessed the challenge of medication management firsthand. “When I was a boy, my mother would call my granny every day and ask if she had taken her pills,” he says. “My granny didn’t like being checked up on all the time — but my mother thought, rightly, that she needed reminding.”

Medication dispensing robot supports independent living

Though that was a while ago, the issue of medication management hasn’t gone away. In fact, it’s estimated that approximately 50% of medicines are not taken as prescribed. That’s a vicious cycle because when conditions are inefficiently managed, it harms patient health and puts an extra burden on already over-stretched health systems.

“The population is ageing,” notes Apell. “So, we should find ways to care for more patients with fewer healthcare professionals.” With this in mind, Apell founded Evondos, a Finland-based healthcare technology company, which has created a medication dispensing robot for the home.

This automated tabletop service — once plugged in — reminds patients when it is time to take their tablets and delivers their medicine to them with clear, spoken instructions. If medication is missed, the robot stores it in a locked chamber, which can only be accessed by care staff. The device can help people with complex medication needs live independently in their own homes.

Automated dispensing would give carers
more time to deliver real, human care.

Improving medication management and preventing error

The technology is currently being used by over 600 home care organisations in the Nordic countries and the Netherlands. Plans are underway to introduce it into the UK market. To date, Apell says it has dispensed more than 30 million medical doses with a 99% adherence rate.

Yet, Apell stresses that automation does not simply improve medication management. It can prevent errors from creeping into sometimes complex medication regimens, too. “If a caregiver is giving patients 10 different pills in 10 different packages, the probability for error is high,” he says. “Automation can make a real impact by preventing human error.”

Freeing carers for human interaction

Plus, he believes it can improve the lives of carers. “I remember my grandmother’s nurses being so busy sorting out her pills that they always didn’t have time to stop and talk to her,” says Apell. “Automated dispensing would give carers more time to deliver real, human care to the people who need it most.”

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Robot bronchoscopy technology can revolutionise the way that lung cancer is detected and diagnosed — and offers the potential of immediate treatment for some patients.

When diagnosing lung cancer, speed is of the essence — it’s vital to find and biopsy cancerous nodules as early as possible.

“If we can treat a tumour when it is less than 10mm in size, the cure rate is more than 92%,” explains Professor Pallav Shah, consultant physician in respiratory medicine, Royal Brompton Hospital, part of Guy’s and St Thomas’ NHS Foundation Trust. “Between 10mm and 20mm, the cure rate is around 83%. However, if the growth is more than 30mm with spread to lymph nodes, the cure rate decreases to the low 40%.”

Robotic bronchoscopy improves lung tumour diagnosis

There are issues locating small lung tumours with a traditional bronchoscopy, which involves inserting a camera into the airways via the mouth. Standard bronchoscopes lack manoeuvrability and accessibility to outer areas of the lungs.

With traditional computerised tomography (CT)-guided transthoracic lung biopsy where the needle is inserted through the chest, there is also an increased risk of puncturing the lung.

However, since April 2023, Professor Shah and his team have utilised new robotic technology, — the Ion Endoluminal System — which he hails as a ‘game-changer’ for earlier detection and treatment of small cancerous nodules.

If we can treat a tumour when it is
less than 10mm in size, the cure
rate is more than 92%.

Many benefits of robotic biopsy technology

Using a robotically controlled catheter system, the tool (which enters the airways via the mouth) is extremely flexible and can move 360 degrees. “With Ion, we can now reach the periphery of the lung and biopsy hard-to-reach nodules,” says Professor Shah.

“Navigation is also made easier because the technology creates a 3D route map, which enables us to precisely find and lock on to small nodules. Previously, when trying to biopsy a nodule of less than 20mm, our success rate was around 65%. The new technology has increased this to 97%.”

The procedure, which also decreases the risk of lung puncture, is performed under general anaesthesia, takes around 25 minutes and enables patients to go home two hours later.

Enabling immediate nodule treatment

Professor Shah and his team are trialling the possibility of using robotic technology to provide immediate treatment when suspicious nodules are found. “This technology has been designed as a diagnostic tool,” he admits. “However, with seven patients, we have used it to find and immediately ablate nodules using a new microwave catheter. technology — a type of ablation tool called the MicroBlate Flex, from UK company Creo. This a world-first, barrier-pushing development — although still in the study phase.”

He says the new technology has been revolutionary. “But, I’m pragmatic and think there is space for different methods of nodule identification and biopsy,” he notes. “We want as many tools as possible, so we can do what is best for patients.”

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Pathologists work across 17 specialties, including haematology and cellular pathology, and carry out millions of tests daily. They are involved in 95% of patient care pathways in the NHS.

How AI can enhance diagnostic landscape

The advent of artificial intelligence can transform the diagnostic landscape, thus improving health outcomes and shortening waiting lists. While the introduction of AI in pathology is exciting, and the NHS could be a world leader in the development and use of AI in pathology, substantial work is required to fully develop AI for safe use.

Investment in digital pathology, integrated IT systems facilitating cross-organisational information sharing and pathologist training for proper AI use are essential steps.

AI can free up time for pathologists, improve laboratory workflow, enhance the accuracy of diagnosis and make patient pathways more efficient — but it will not replace the need for pathologists. They will still need to interpret and analyse information to produce an overall pathological report and provide a diagnosis and treatment plan.

Automated iLFT speeds up liver disease care pathway

The intelligent liver function testing system (iLFT), for example, is an automated system designed to improve the early diagnosis of liver disease. It relies on algorithmic processes to automatically assess blood samples for further testing when liver disorders are suspected, which a pathologist then validates.

AI can free up time for pathologists, improve laboratory workflow, enhance the accuracy of diagnosis and make patient pathways more efficient.

As opposed to standard liver function testing (LFT) involving a range of tests, which are usually repeated several times before patients are referred to specialist treatment, iLFT contains additional tests. These additional tests are prompted by the algorithm and immediately added automatically as further tests. This means that no further samples need to be sent, reducing the number of visits for the patient to undergo additional blood tests.

iLFT speeds up referral, diagnosis and disease management and has benefited over 25,000 patients in NHS Tayside, with 30% receiving early treatment. However, replicating this across the UK will require IT investment to ensure its integration within all computer systems.

Investment in digital pathology

Adopting AI for use in pathology services depends on investment in digital pathology, which covers the collection, management, sharing and interpretation of pathology information — for example, blood tests and biopsies — in a digital environment. Developing functional and interoperable IT systems that can interrogate and seamlessly exchange information will also be essential. Only with this investment will the NHS and patients benefit from the advent of AI.

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A recent survey shows widespread patient enthusiasm for adopting self-testing and AI technologies to speed diagnoses and access to healthcare services.

A survey by the Patients Association found patients eager to adopt innovative diagnostic methods like self-testing and artificial intelligence (AI) to improve their access to healthcare and speed up results. 

At-home diagnostics and self-testing

The survey uncovered overwhelming support for expanding at-home testing opportunities, with three out of five (61%) respondents believing it could speed up diagnosis. This enthusiasm may be a consequence of people’s increased familiarity with self-testing during the Covid-19 pandemic.

When asked about where patients would be willing to self-test, three-quarters (77%) said they would be comfortable testing at home, compared to two-fifths (44%) who would self-test in a clinical setting. Just 4% were unwilling to self-test in either location.

This preference for home self-testing may stem from patients’ confidence in their ability to test without supervision. Some responses suggest patients see self-testing as a gateway to necessary services. Hesitation around clinical self-testing could reflect a belief clinicians should administer tests.

Three-quarters (73%) said they
were very or quite likely to trust
diagnostic results generated by AI.

Leveraging AI to accelerate diagnosis

The survey, supported by Roche Diagnostics UK and Ireland, also explored attitudes toward using AI in diagnostics. Three out of five (60%) felt leveraging new technologies like AI could accelerate diagnoses and relieve strain on the healthcare system.

Encouragingly, three-quarters (73%) said they were very or quite likely to trust diagnostic results generated by AI. However, the two-fifths (44%) who were ‘quite likely’ hint at lingering doubts for some. Only 16% were outright distrustful of AI-powered results.

Lifestyle changes to manage conditions

For conditions like Alzheimer’s disease, our diagnostic capabilities currently outpace treatments. Still, nearly three-quarters (72%) said they would take a predictive test if available, and nine out of ten (89%) would make lifestyle changes to potentially delay onset of disease or symptoms if results said they were at high risk.

These data reveal patients are ready for innovations like self-testing and AI to improve diagnostic processes. As these technologies advance, the healthcare system must work with patients before deploying them, addressing public concerns and delivering new services they will want to use and benefit from.

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Gaelan Komen

Policy and Research Manager, Policy Connect

Nathan Wood

Managing Director, Farmwood

Atmospheric monitoring technologies can bring healthcare into the home and improve patient outcomes.

Where should a patient pathway start? When sickness is caused by the household environment, healthcare must begin at home. Indoor environments are incubators of illness; the Covid-19 pandemic can be considered a network of poorly ventilated indoor spaces, and poor-quality housing is increasingly being linked to ill health and premature death.

Household environments and ill health

The death of two-year-old Awaab Ishak in December 2020 from mould in his home is a tragic illustration of the lethality of the home environment — and the need for cross-sector collaboration to improve health at home.

Awaab’s family lived in a mouldy and inadequately ventilated one-bedroom flat in Rochdale and had little support to tackle the toxic air indoors. Housing services, despite visiting the home, stated they didn’t recognise the level of risk to health, and the coroner found information gaps between local and health services.

With various local services entering homes, many professionals — from care workers to gas engineers and housing surveyors — have a role to play in ensuring health at home.

Household monitoring should be a fundamental
component of the patient pathway.

Need for widespread household monitoring

Mould is easy to spot, but to assess and address the invisible cocktail of airborne toxins indoors, atmospheric monitoring technologies are essential.

Innovative smart monitoring systems have recently been developed, which can pinpoint pollution levels, identify sources and provide actions to improve air quality – but these may be difficult to widely introduce. To assess risk in vulnerable homes like Awaab’s, frontline workers should be equipped with wearable monitoring devices.

For effective care and better outcomes, household monitoring should be a fundamental component of the patient pathway, enabling rapid testing, assessment of health risk and evidence-based referral for healthcare or home upgrades.

How to ensure indoor air quality

The Chief Medical Officer Chris Whitty has highlighted household air pollution as an urgent public health priority. However, a lack of data frustrates policy action while a lack of policy hinders data collection.

We call on the UK Government to take the initiative and institute a national indoor air quality observatory. Such systematic monitoring — as is in place in schools, homes and care settings in France — can uncover vital unknowns about indoor air pollution and guide action to tackle hazardous living conditions.

The Government is committed to improving the quality of social housing through ‘Awaab’s Law.’ To successfully address hazards like mould, inequalities in exposure and ill health, the Government must join up health and local public services through systematic air quality monitoring in homes.

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