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Take a look at a selection of our recent media coverage:
24th February 2025
As the UK’s first clinical professor of AI in radiotherapy, Professor Raj Jena talks to Helen Quinn about the impact of artificial intelligence and deep learning tools on radiotherapy and patient care, the opportunities and challenges he’s hoping to tackle in his new role, and the broader potential of AI in healthcare.
Put simply, artificial Intelligence (AI) has the potential to transform healthcare. Effective use of emerging machine learning techniques can improve patient care, complement clinicians’ work and address a range of challenges. If used well and in the proper context, AI can enhance diagnostic processes, personalise treatment plans and efficiently manage healthcare data, all while freeing up clinicians’ time to focus on the direct human aspect of healthcare.
Within this rapidly evolving technology landscape, the University of Cambridge has appointed the UK’s first clinical professor of AI in radiotherapy, signalling a need for, and a commitment to, utilising AI in the fight against cancer. Taking up this novel role is Professor Raj Jena, who is also a research scientist and consultant oncologist at Cambridge University Hospitals NHS Foundation Trust.
Professor Jena specialises in using advanced imaging techniques to improve outcomes for patients with central nervous system tumours. Through his research, he has helped to develop an AI tool called Osairis, which can enhance and accelerate tumour analysis.
Machine learning for radiotherapy is now routinely used throughout Cambridge University Hospitals NHS Foundation Trust. It has reduced the waiting time for patients between referral and commencing curative radiotherapy treatment, which can, in turn, improve survival rates in some patients.
The new AI clinical professorship reflects the progress in balancing clinical practice and domain expertise in radiotherapy whilst maintaining and leading an academic group delivering high-quality research, says Professor Jena.
‘We’re trying to link the latest and greatest thinking in data science, machine learning and AI to what we do in the clinic,’ he explains. ‘Most people think an oncology consultant who’s active in research would either be working in a wet lab or work in the area of clinical trials. So, it’s quite nice to identify the fact that there is another way an academic oncologist can contribute to research.’
In fact, over the past 20 years, Professor Jena has concentrated on using mathematics and computation to analyse medical images – something he says has been recognised in the new clinical professor of AI in radiotherapy role. ‘I’ve been interested in computational approaches for years, but nowadays it’s reached the mainstream, and it’s called AI. It’s great because we can ride the wave of interest in AI,’ he says.
The use of AI in medical imaging involves applying a deep learning model to perform clinically valuable tasks. This is particularly applicable to analysing radiotherapy images, making it a highly effective technique in this field.
‘If you look at clinically useful applications of AI across the whole of medicine, the reality is that we’re still at the start of that story. But in radiation therapy, we happen to have a problem that lends itself to a solution in deep learning,’ Professor Jena explains. ‘We’ve gone quite quickly, from these approaches being just research to actually being plumbed into the clinic and helping patients get started on potentially life-saving radiotherapy more quickly.’
The development of the Osairis tool stemmed from a chance meeting between Professor Jena and Dr Antonio Criminisi PhD, a machine learning engineer and the head of Microsoft’s AI research programme in the UK.
Dr Criminisi taught computers to analyse the movement of the human body from the outside, recognising specific positions so a person’s body could be used effectively as a controller, for example, in sports-related video games. Professor Jena was curious whether this approach could be applied inside the body, too, and invited Dr Criminisi to his hospital department to observe radiation oncologists marking up scans of patients waiting to start radiotherapy treatment.
The outcome was the development of an open-source deep learning tool for automatic segmentation of radiotherapy images and the first AI technology to be developed and deployed within the NHS.
‘It was a very prescient point, we could then take the tooling and actually build our own machine learning models from our own patients’ data, test them out, and then for the first time, within the hospital, build a medical device,’ Professor Jena explains.
Cambridge University Hospitals Trust invested in cloud computing across its sites, allowing Professor Jena’s team to implement deep learning tools throughout the Trust. Now, when a patient with a head, neck or brain tumour comes for a scan, the scan data is anonymised, encrypted and sent off for analysis using the AI technology. It has been found to accelerate clinicians’ radiotherapy planning by approximately two and a half times.
‘What the algorithm does is to mark out every healthy structure we need to be aware of when planning radiotherapy treatment. And that means that the oncologist can be much faster in creating a safe radiotherapy plan,’ Professor Jena says. ‘Something that used to take maybe an hour and 40 minutes can be done in half an hour, so you can see patients faster and free up clinicians and patients get started on radiotherapy more quickly, too.’
Despite the myriad ways in which AI can support healthcare systems, challenges remain. Many machine learning models are built based on available data rather than in response to a particular patient need, and the data required to build specific models can be difficult to obtain.
Professor Jena says turning available data into necessary data requires considerable effort. He hopes his new professorship, which straddles both the research and clinical environments, will help him achieve this as he builds AI tooling to address specific patient needs and avoid bias in the system.
AI technology is also moving rapidly, and the journey has sometimes involved missteps, including breaches of data usage and sharing of data with industry. Professor Jena warns that robust governance needs to be in place to prevent further issues, particularly as AI models begin incorporating more sensitive data, such as genomic information.
‘I think we have to take those things and learn how to do it better. The biggest thing we can do is make examples where we do this right, that are highly shareable and highly applicable,’ Professor Jena says.
Radiotherapy is an exemplar of the successful use of AI in healthcare, but Professor Jena hopes there will be a cross-fertilisation of technology, enabling AI to evolve and excel at interpreting non-image data as well. He believes AI can ‘make real inroads’ in diagnostics for the early detection of cancer. Early works suggest it could be used in tests that can look for cancer in urine, blood or even exhaled breath, for example.
The tools could also play a role in personalising treatments for cancer patients since AI can look for patterns and simplify very high-dimensional data. In complicated cancers such as brain tumours, where several medications might be marginally effective, an AI model could examine that information, align it with changes in the patient’s tumour and suggest a personalised medication plan.
‘I think this is where we really want to push,’ says Professor Jena. ‘Personalised medicine is very interesting to us because we now get so much information when a cancer is diagnosed, including genomics, which can highlight mutations and indicate a patient may benefit from some kind of targeted drug. I think the paradigm changes around AI in medicine will come within the areas of precision medicine or drug discovery.’
Ultimately, Professor Jena says that AI will complement and enhance much of what clinicians already do, freeing them from time-consuming, data-heavy tasks.
‘As you build these workflow acceleration tools, all staff will move towards a situation where they’re spending more time either listening to patients directly or making decisions. I think that will make a huge difference,’ he says. As well as awaiting the paradigm shift in AI, I’m a great believer in bringing together multiple AI tools where each one saves time or increases safety. Adding up all of these small increments can still make a huge impact on the delivery of human-centric care in the clinic.’
23rd December 2024
The NHS is using artificial intelligence (AI) to identify patients likely to become frequent users of emergency departments (EDs) across England so it can be proactive about their care and reduce demand on emergency services.
High Intensity Use (HIU) services have been rolled out to 125 EDs across England so far. These use AI-powered prediction software on routinely collected hospital data to identify the most regular attendees to EDs in their area.
These patients are then offered one-to-one coaching in their own homes to tackle the root cause of why they are visiting an ED.
Preventative care includes supporting patients with long-term conditions, like asthma or diabetes, and having a healthcare professional reach out to them to offer them personalised, preventative support and self-management techniques.
It has already helped reduce the number of frequent attendances by more than half in some parts of the country.
Over 360,000 patients attend EDs more than five times every year.
Amanda Pritchard, NHS England chief executive, said: ‘We know that a small proportion of the population are much more likely to use A&E or ambulance services, so it is important we give them the targeted support they need this winter before they get to the front door of an emergency service – this is much better for them but will also help to relieve pressure on the NHS.
‘Initiatives like using AI to spot those who may need extra support in the community help provide more personalised care and must be central to our 10 Year Health Plan.’
South Tees Hospitals NHS Foundation Trust recruited a dedicated keyworker to offer social, practical and emotional support to 20 high-use patients, which resulted in them halving their emergency department visits – down from 33 times per year.
Black Country Healthcare NHS Foundation Trust created a HIU service at New Cross Hospital in Wolverhampton to recognise people in crisis and improve the health outcomes of those who face the highest deprivation and health inequalities in their area.
The service, combining community outreach with a dedicated clinical lead, led to a significant improvement in the wellbeing of frequent attenders and a reduction in hospital attendance by almost three fifths (58%).
Norfolk Community Health and Care NHS Trust also established a HIU service and worked closely with over 400 of the most frequent attenders to EDs.
This included one service user who suffered from a serious condition and was regularly visiting the emergency department, sometimes as often as twice a week.
The HIU service helped him to access the correct support, deal with his housing issues and start looking for work again.
Since the HIU service involvement, he has not visited the emergency department or dialled 999 again, contributing to the Trust’s 58% reduction of A&E attendances from frequent attenders and a reduction in hospital admissions of 62%.
Health Minister Karin Smyth said the HIU services offered ‘a double win for getting vulnerable patients the right support and saving the precious time of busy A&E staff’.
In February, a clinical trial at Amsterdam UMC aimed to show that an AI algorithm could reduce the number of blood cultures taken in EDs worldwide by almost a third.
A version of this article was originally published by our sister publication Healthcare Leader.
12th December 2024
A machine learning model can more accurately estimate the age of acute ischemic brain lesions than current methods, with researchers predicting the software could mean up to 50% more stroke patients receive appropriate treatment.
The efficacy and appropriateness of stroke treatment depended on the progression stage or biological age of the lesion and whether it was deemed to be reversible, researchers wrote in the journal NPJ Digital Medicine.
‘Biological age is closely related to chronometric lesion age – i.e. time from symptom onset – although these ages disassociate due to variability in tissue vulnerability and arterial collateral supply,’ they said.
Acute ischemic lesions scanned with non-contrast computerised tomography (NCCT), become progressively hypoattenuated over time, the research team explained, a feature which helped to estimate biological lesion age.
At present, clinicians measured the relative intensity (RI) of a lesion on NCCT using a method termed Net Water Uptake (NWU).
However, the researchers noted this approach could be confounded by alternative sources of hypointensity, was also insensitive to additional ischemic features and dependant on lesion segmentation.
For this trial, researchers from Imperial College London and University of Edinburgh, UK, and the Technical University of Munich, Germany, developed a convolutional neural network – radiomics (CNN-R) model to optimise lesion age estimation from NCCT.
They noted that machine learning models had several advantages over current methods for stroke assessment such as the ability to screen high-dimensional imaging features for associations with ischemia progression, including those imperceptible to experts, as well as account for lesion anatomy variability and signal heterogeneity.
They trained the CNN-R model on chronometric lesion age, while validating against chronometric and biological lesion age in external datasets of almost 2,000 stroke patients.
Analysis showed the deep-learning model was approximately twice as accurate as NWU for estimating chronometric and biological ages of lesions.
‘The practical importance of our results lies in the CNN-R lesion age biomarker providing more accurate estimates, compared to current methods, of stroke onset time (unknown in ~20% of cases), and lesion reversibility, both currently used for decisions regarding revascularisation treatments,’ the researchers wrote.
As well as validating the method in a large, independent cohort, the researchers said they had demonstrated the technique could be embedded within a central pipeline of automated lesion segmentation and clinically-based expert selection.
Future research should assess whether the higher accuracy of a CNN-R approach to lesion age estimation carries over to predicting lesion reversibility and functional outcomes, they added.
Lead author Dr Adam Marcus, of Imperial College London’s Department of Brain Sciences, estimated up to 50% more stroke patients could be treated appropriately because of this machine learning method.
‘We aim to deploy our software in the NHS, possibly by integrating with existing artificial intelligence-analytic software that is already in use in hospital Trusts,’ he said.
Study senior author Dr Paul Bentley, of Imperial College London’s Department of Brain Sciences and consultant neurologist at Imperial College Healthcare NHS Trust, said the information would help clinicians make emergency decisions about stroke treatment.
‘Not only is our software twice as accurate at time-reading as current best practice, but it can be fully automated once a stroke becomes visible on a scan,’ he said.
The study follows research released last month showing artificial intelligence-enabled electrocardiography (ECG) can accurately predict an individual patient’s risk of future cardiovascular events as well as their short and long-term risk of dying.
Lead author of this study Dr Arunashis Sau, an academic clinical lecturer at Imperial College London’s National Heart and Lung Institute and cardiology registrar at Imperial College Healthcare NHS Trust, said compared with cardiologists the AI model could detect more subtle detail in the ECGs.
11th December 2024
Three quarters (75%) of the public support sharing some of their personal health data to aid the development of artificial intelligence (AI) systems in the NHS, a Health Foundation report has found.
The research surveyed 7,201 people across the UK between June and July 2024 and found that 29% were happy for any of their health data to be used to develop AI systems and a further 46% were happy to use ‘some’ of their data for this purpose.
A quarter of people (25%) however were not happy to share any of their data for this purpose.
When surveyed about the type of data being shared, 59% were willing to share data on eye health, 58% on the medicines they are taking and 57% were willing to share long-term conditions, dental health and medical test data, such as blood tests or scans.
However, less than half (47%) were willing to share smartphone-tracked data, such as sleep activity, and only 44% were happy to share sexual health information.
Those from lower socio-economic households were significantly less likely to support the use of their health data for developing AI technologies compared to those from other socio-economic groups.
Those of a higher managerial level, administrative or professional in their work, were more likely to think that using technology makes the quality of healthcare better, with 65% of this group saying this, compared to just 41% of casual or lowest-grade workers and others who depend on the welfare state for their income.
The survey also showed that most people trust the NHS with their health data, with 68% saying they trust GP practices and 66% stating trust in local NHS hospitals and clinics.
However, only 40% said they trusted companies providing the NHS with software to collect, store and use data.
The report called for policymakers and NHS leaders to ‘actively engage with the public to understand and address concerns’, especially with social groups who are less supportive.
Director of innovation and improvement at the Health Foundation, Dr Malte Gerhold, said: ‘It is only with the public’s support that the Government will successfully achieve its ambition of shifting the NHS from analogue to digital.
‘It is encouraging that most people are open to sharing their data to develop AI systems in the NHS. When properly implemented, we know that AI has the potential to free up staff by supporting clinical and administrative tasks. However, these systems are only as good as the data used to design and develop them.’
However, he added: ‘There are significant differences between socioeconomic groups in levels of support for sharing data for AI development and for taking part in activities to shape how technology is used in the NHS.
‘Policymakers, NHS leaders and those involved in designing and implementing healthcare technologies must proactively engage with people across different social groups to ensure that healthcare technologies help tackle inequalities, rather than worsen them.’
In July, a survey from the Health Foundation revealed that NHS staff and patients are both split on the use of AI in healthcare.
And in April a white paper from the Microsoft company Nuance reported that over a third of patients are in favour of clinicians using AI in consultations to improve documentation processes such as clinical letters.
A version of this article was originally published by our sister publication Healthcare Leader.
2nd December 2024
Staying abreast of developments in artificial intelligence and machine learning is becoming increasingly important for the delivery of timely, efficient and cutting-edge healthcare, but that can be a challenge. Data science academic Dr Russell Hunter PhD looks at the top trends that healthcare professionals and their organisations need to know about as they navigate this rapidly evolving landscape.
There has long been a widespread interest in how artificial intelligence (AI) and machine learning (ML) could transform the healthcare sector. For example, common searches on Google include questions such as ‘How is machine learning used in healthcare?’ and ‘Does the NHS use machine learning?’.
The interest was taken up a notch recently when the Government committed to a digital-first NHS following critical concerns raised in the Darzi report. Yet, although AI and ML are reshaping everyday practices within healthcare, questions – and perhaps scepticism – remain. And it can be hard for healthcare leaders to address concerns when they are not experts and AI is evolving so fast.
So, what do leaders need to know in terms of emerging trends in AI and ML, and how can those who are suspicious of AI be convinced that it can be a help rather than something to be worried about?
Explainable AI, also known as XAI, aims to make AI decisions understandable to humans, enhancing trust and regulatory compliance.
When a model is built to solve a particular problem, persuading stakeholders to come on board can often be difficult. In fact, many would prefer a model that is more easily understood, even if it is less optimal. Something that can be visualised is preferable to jumping on board with a mysterious model that works for unknown reasons. This is especially important when it comes to healthcare or finance.
In healthcare, XAI provides explanations for diagnostic decisions or treatment recommendations made by AI systems. These explanations are crucial for doctors and patients to trust and act on AI-driven insights, ultimately improving patient outcomes. AI models used for predicting patient risks, such as the likelihood of developing a certain disease, need to be clear and understandable to ensure that healthcare providers can grasp the underlying factors behind the risk assessment.
Autonomous systems are transforming healthcare by accelerating the speed and precision of decision-making, driving greater efficiency and enhancing customer experiences. In the business world, ML technologies can increase companies’ ability to quickly analyse vast amounts of data while uncovering patterns and making informed decisions.
Just as automating manual processes can help make sense of business data, advanced systems can be applied to healthcare. Sophisticated multimodal AI can analyse genetic data and patient histories to recommend personalised treatment plans. This leads to more effective and individualised healthcare.
Similarly, by leveraging data from electronic health records, these systems can predict patient outcomes or complications, which allows for proactive intervention.
Agenetic AI is a new class of AI designed to act with autonomy. It proactively sets its own goals and takes autonomous steps to achieve them, making decisions and taking action without direct human intervention. This makes it a significant advancement beyond classical reactive AI.
These proactive systems can enhance patient care and have the potential to alleviate the burden on healthcare professionals by automating routine monitoring and treatment adjustments.
In the realm of personalised healthcare, agentic AI can revolutionise patient care by continuously monitoring patient health metrics and autonomously administering medication as needed. For example, an agentic AI system could monitor the blood sugar levels of a patient with diabetes in real-time and administer insulin precisely when required, thus maintaining optimal glucose levels and reducing the risk of complications.
Agentic AI can also help with personalised treatment plans for chronic diseases by analysing vast amounts of patient data to predict disease progression and suggest tailored treatment plans. For instance, in oncology, agentic AI can process data from medical records, genetic profiles and treatment responses to recommend personalised chemotherapy protocols, potentially improving outcomes and minimising side effects.
Another cutting-edge development is Edge AI, which brings an immediate processing capability crucial for applications in healthcare monitoring where time-sensitive tasks require prompt responses. This is achieved by processing data locally on the device, reducing latency, enabling real-time decision-making and minimising the amount of data that needs to be transmitted to central servers.
Processing sensitive information locally also enhances privacy and security, reducing the risk of data breaches during transmission, which is particularly important with healthcare data.
However, there are challenges. There are hardware limitations and integration complexity, and there is a need for efficient management and maintenance of numerous edge devices. These could curtail the full effectiveness of edge AI.
While there are concerns that AI will replace humans in the workplace, the latest AI developments can augment rather than undermine human contributions. For example, AI can assist doctors by analysing medical images and patient data to identify patterns that the human eye might miss. This allows doctors to make more accurate diagnoses and develop personalised treatment plans, thereby improving patient outcomes and operational efficiency.
The collaboration between humans and AI combines the strengths of both, allowing AI to handle repetitive, data-intensive tasks while people focus on strategic, creative and interpersonal activities that require emotional intelligence and critical thinking. This applies to healthcare as much as any other sector.
Rather than eliminating jobs, AI reshapes them. As technology advances, new roles will be created where the job is managing, programming and collaborating with AI systems. It is crucial to keep an eye on developments to ensure healthcare organisations are fully equipped to gain an edge by leveraging AI and ML.
Dr Russell Hunter has a PhD in Computational Neuroscience and works at the University of Cambridge. He leads the course Leveraging Big Data for Business Intelligence at Cambridge Advance Online.
A version of this article was originally published by our sister publication Healthcare Leader.
1st November 2024
Artificial intelligence (AI)-enabled electrocardiography (ECG) can accurately predict an individual patient’s risk of future cardiovascular events as well as their short and long-term risk of dying, a study finds.
Existing AI-enabled ECG could predict disease and mortality but could not give sufficient information to guide clinical decisions for individual patients and were not adopted into practice, UK researchers wrote in The Lancet Digital Health.
To address the limitations of previous AI models, the team from Imperial College London and Imperial College Healthcare NHS Trust developed the AI-ECG risk estimator (AIRE) platform using data from a secondary care dataset of 1,163,401 ECGs taken from 189,539 patients.
Using a deep learning and a discrete-time survival model, researchers were able to create a patient-specific survival curve with a single ECG, allowing the AIRE platform to predict not only risk of mortality but also time-to-mortality.
The platform was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK, including volunteers, primary care patients, and secondary care patients.
Researchers found the model was able to identify the risk of death in the ten years following the ECG (from high to low) in 78% of cases, and was also able to predict future ventricular arrhythmia, future atherosclerotic cardiovascular disease, and future heart failure.
‘Through phenome-wide and genome-wide association studies, we also identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome,’ they wrote.
They concluded that clinicians could act on AIRE’s predictions to provide targeted, personalised and earlier intervention.
‘AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation,’ they said.
Lead author Dr Arunashis Sau, an academic clinical lecturer at Imperial College London’s National Heart and Lung Institute, and cardiology registrar at Imperial College Healthcare NHS Trust, said compared with cardiologists the AI model could detect more subtle detail in the ECGs.
‘It can ‘spot’ problems in ECGs that would appear normal to us, and potentially long before the disease develops fully,’ he said.
‘Our analysis shows that the AI can tell us a lot about not only about the heart but also what is going on elsewhere in the body and may be able to detect accelerated ageing.’
Dr Sau acknowledged it was necessary to see how the model performed in the healthcare system, but suggested it was possible that in the future, the technology could be used in a wearable device that provided doctors with continuous remote monitoring and a potential alert system.
Senior study author Dr Fu Siong Ng, Reader in Cardiac Electrophysiology at the National Heart & Lung Institute at Imperial College London, said the work had shown the AI model was a credible and reliable tool that could, in future, be programmed for use in different areas of the NHS to provide doctors with relevant risk information.
‘This could have a positive impact on how patients are treated, and ultimately improve patient longevity and quality of life, he said.
Dr Ng, who is also a consultant cardiologist at Imperial College Healthcare NHS Trust and Chelsea and Westminster Hospital NHS Foundation Trust, said the technology could also reduce waiting lists and allow more efficient allocation of resources.
Trials of the model are planned to start by mid-2025 in hospitals across Imperial College Healthcare NHS Trust and Chelsea and Westminster Hospital NHS Foundation Trust.
Participants are set to be recruited from outpatient clinics and from inpatient medical wards.
30th October 2024
With another successful ESC Congress under its belt, the European Society of Cardiology’s new president Professor Thomas Lüscher speaks to Helen Quinn about the current challenges and opportunities in European cardiology, his highlights from the congress and his thoughts on the future of cardiovascular care.
In September 2024, at the European Society of Cardiology (ESC) annual congress, delegates welcomed Professor Thomas F. Lüscher as their new president and it’s a role he is excited about taking on.
Professor Lüscher is a world-renowned cardiologist, ranking in the top 0.5% globally of most cited scientists and currently a consultant cardiologist and director of research, education and development at the Royal Brompton and Harefield hospitals in London and professor at King’s College London, UK.
Having been involved with the ESC for many years, Professor Lüscher has chaired various working groups, became vice president in 2003 and then editor-in-chief of the European Heart Journal in 2008 – a position he held for 11 years. He describes the society as ‘a fantastic success story’ that has evolved from ‘a small club of friends into the largest and most influential society in medicine’.
With seven associations, seven councils, 15 working groups, 57 national societies, 47 affiliated national societies, 17 journals, 18 textbooks, an annual congress and nine speciality congresses, the ESC works hard to improve cardiovascular care and patient outcomes throughout Europe.
‘[It’s] an institution that dominates the field in a positive manner by providing guidelines, education and registries to improve the burden of cardiovascular disease. So, it’s a really exciting position I have,’ Professor Lüscher says.
Cardiovascular disease is still the leading cause of morbidity and mortality in Europe, and there are significant challenges facing the field. In the past, support from the EU has favoured oncology over cardiovascular healthcare. To try to change this imbalance, the ESC has responded by putting together a cardiovascular health plan, which has been submitted to the EU Council of Health Ministers to raise the profile of research and increase the quality and equality of care patients receive.
‘We hope this will impact the support for cardiovascular science and education in the future,’ Professor Lüscher says. ‘Europe has had a fantastic history. Most of the interventions have been invented in Europe, starting with pacemakers, atrial fibrillation ablation, percutaneous coronary intervention, transcatheter aortic valve implantation and MitraClip. It’s quite an amazing story.’
Today, however, innovation and development are hampered by regulations, according to Professor Lüscher. At the same time, the Food and Drug Administration in the US has become more lenient and much quicker and effective in approving drugs and trials.
‘I’m concerned that the speed and impressive innovation we have delivered over the last 200 years may be fading a little bit. There has been a bit of a shift from Europe to the US. [There are] a lot of rules and regulations in the EU and the UK,’ explains Professor Lüscher.
A lack of centralised device regulation in Europe is also impeding developments in field. Consequently, the ESC is working constructively with the European Medicines Agency and the Notified Bodies to make Europe fitter for innovation.
For some patients, differences in access to care is one of the main barriers to improving cardiovascular health across the continent. Such inequalities are highlighted in the ESC’s publication ‘Atlas of Cardiology’, which gives a picture of the current state of cardiovascular across Europe and shows vast differences in modern management options for cardiovascular conditions in different countries.
Patients in countries like Germany, Switzerland, Scandinavia and the Netherlands have good access to the latest treatments and medications. In other European countries, access is much more difficult, with many patients – particularly those in Eastern Europe – missing out.
And in the UK, for example, there is a concern that lower social classes have limited access to the latest cardiovascular treatments, Professor Lüscher explains, with deprived areas experiencing worse levels of care and, in turn, worse outcomes.
‘If you have severe heart failure, you might need a left ventricular assist device and in many countries that’s not available. Also, some novel, more expensive drugs are not available in certain countries,’ Professor Lüscher says. ‘There’s a huge heterogeneity in access to treatment across European countries. These are ethical concerns for medicine that, by nature, is a humanistic profession. The ESC tried to address this problem.’
The European Union has tried to overcome these inequalities in care by putting pressure on the prices of medications. There is also pressure on patent durations to make generic therapies available more easily and earlier, which is beneficial in the short term, but it is something that Professor Lüscher worries will obstruct innovations in the long term.
‘In the end, this is an economic problem,’ Professor Lüscher says. ‘There’s a close correlation between gross national product and availability of medical services, and currently in Europe the economy is not doing well. In many countries, we have issues with the economy that reflect on the service for patients.’
There is, however, much to be excited about in the field of cardiology, with many innovations and new research shared at the ESC Congress 2024. For Professor Lüscher, two significant potential developments excited him the most.
The first is the development of genetic tools as therapeutic agents to treat and prevent cardiovascular disease. This cutting-edge approach focuses on the use of antisense oligonucleotides (ASOs) and small interfering RNAs (siRNAs), which can block the production of certain proteins in the body and currently mainly target the liver.
‘The liver has specific receptors, in particular the asialoglycoprotein receptor, mainly expressed in hepatocytes. So, once linked with a GalNac residue, you can direct these double-stranded RNAs specifically to the hepatic cells. Then they bind to the RISC complex within the cell and inhibit the translation of a transcript to a protein over several months,’ Professor Lüscher explains.
This process enables a long-lasting therapeutic approach. There are now siRNAs for PCSK9, which lower low-density lipoprotein (LDL) plasma levels for six months, and others, including a new development for lipoprotein(a). In addition, siRNA therapies can target angiotensinogen to lower blood pressure for several months. Other siRNAs, like those that reduce transthyretin (TTR), help treat ATTR amyloidosis by preventing the formation of harmful amyloid deposits.
Gene editing tools, such as CRISPR-Cas9, are also emerging, which can precisely modify nucleoid acid sequences in the DNA. In animal trials, this tool has been used to permanently block the production of PCSK9, preventing it from binding to LDL receptors and thus lowering cholesterol levels and potentially offering a one-off, lifelong treatment.
‘The long-term vision is that we cure rather than treat. These genetic tools are a completely new chapter in pharmacotherapy,’ Professor Lüscher says.
A second area of innovation that will continue to be incredibly influential in cardiovascular medicine is the development of artificial intelligence (AI) and machine learning. As part of his presidency, Professor Lüscher has set out his vision for the digital transformation of cardiology in Europe.
Beginning with online consultations, he believes AI has much to offer clinicians and patients. ‘With an algorithm, you can analyse the face of a person, see the pulse, see the wrinkles, see the amount of sweat, and you can make outcome predictions,’ he says.
‘AI analyses any sort of picture, not just faces, but echocardiograms, CT scans, MRIs, nuclear scans, pathology specimens, biopsies – anything that’s visual and can also diagnose patients,’ he adds.
Analysing the human voice is also possible using AI, which can be incredibly helpful for cardiovascular diagnosis by identifying atrial fibrillation and arrhythmias through variations heard in the vocal cords as well as congestion caused by heart failure.
Professor Lüscher believes AI algorithms will become important ‘co-pilots’ for clinicians, prompting them to think about diagnoses they may have missed. Other algorithms can read reports shared as part of a referral, giving summaries and analysing volumes from images in seconds that would otherwise take clinicians significant chunks of time.
‘It makes us faster and more precise, provided the algorithms are good,’ Professor Lüscher says. ‘Algorithms that are not good or false can potentially kill patients. These algorithms have to travel well and work in different geographical areas and countries, otherwise it’s not acceptable.’
As such, the ESC is involved in developing quality standards for algorithms across Europe and only uses algorithms that they can show are well-verified in independent cohorts.
Amidst the innovations and new pathways, there will inevitably be challenges ahead, but there is much to look forward to in cardiology as Professor Lüscher begins his two-year presidential journey.
8th October 2024
The rapidly developing area of technology and artificial intelligence (AI) within respiratory medicine and science was under the spotlight at this year’s European Respiratory Society (ERS) Congress, including the use of artificial neural networks (ANNs) in detecting bronchopulmonary dysplasia (BPD) in preterm infants.
ANNs can be trained to detect BPD in preterm infants by analysing their breathing patterns, Swiss researchers reported at the ERS Congress.
There is difficulty in identifying BPD with current lung function tests as they require sophisticated equipment, the study authors said.
ANNs are mathematical models which, once trained with large amounts of data, can be used for classification and prediction.
Lead author Professor Edgar Delgado-Eckert, adjunct professor at the Department of Biomedical Engineering at the University of Basel said: ‘Until recently, this need for large amounts of data has hindered efforts to create accurate models for lung disease in infants because it is so difficult to assess their lung function.’
For this study, the researchers used a simpler and non-invasive alternative: measuring an infant’s inspiratory and expiratory air flow during tidal breathing to yield a large amount of sequential flow data which could be used to train an ANN.
Professor Delgado-Eckert’s team studied a group of 139 term and 190 preterm infants who had been assessed for BPD, recording their breathing using a soft face mask and sensor for 10 minutes while they slept.
Among the 190 preterm infants, 47 were diagnosed with mild BPD, 54 with moderate BPD and 31 with severe BPD.
For each infant, 100 consecutive regular breaths, carefully inspected to exclude sighs or other artefacts, were used to train, validate and test a long short-term memory recurrent ANN.
The data was randomly split into 60% for training and 20% for validation, with the remaining 20% given to the model unseen to test if it could identify infants with BPD.
On the unseen test data, the model achieved 96% accuracy, 100% specificity, 96% sensitivity and 98% precision for detecting BPD.
Professor Delgado-Eckert said: ‘Our research delivers, for the first time, a comprehensive way of analysing the breathing of infants, and allows us to detect which babies have BPD as early as one month of corrected age – the age they would be if they had been born on their due date – by using the ANN to identify abnormalities in their breathing patterns.’
ERS Congress co-chair Professor Judith Löffler-Ragg said the research presented at this year’s event under the theme of ‘Humans and machines: getting the balance right’ was pioneering and should guide future developments.
‘It is extremely important that we view developments in technology, and specifically AI, with an open mind but also a critical eye,’ she said.
‘Our vision is to advance personalised medicine through the responsible use of AI, continuously improving respiratory medicine.’
3rd October 2024
The rapidly developing area of technology and artificial intelligence (AI) within respiratory medicine and science was under the spotlight at this year’s European Respiratory Society (ERS) Congress, including the use of large language models (LLMs) such as ChatGPT to assess complex respiratory disease in children.
The LLM ChatGPT performed better than trainee doctors in assessing complex paediatric cases of respiratory disease, a study found, suggesting LLMs could be used to support patient triage.
UK researchers compared the performance of three LLMs (ChatGPT, Microsoft Bing and Google’s Bard) against early-career trainee doctors in providing responses to six paediatric respiratory clinical scenarios. Each scenario had obvious diagnosis and no published evidence, guidelines or expert consensus that pointed to a specific diagnosis or plan.
The 10 trainee doctors were given an hour with internet access, excluding access to LLMs, to solve each scenario with a 200- to 400-word answer.
Responses were randomised and scored by six experts overall and on specific criteria: correctness, comprehensiveness, utility, plausibility, coherence and humanness.
ChatGPT (median overall score 7) outperformed Bard (median 6), Bing (median 4) and the trainee doctors (median 4) in all domains.
Bard scored better than the trainee doctors in coherence, with Bing and the trainee doctors scoring similarly.
The six experts were able to identify Bing and Bard’s responses as non-human, but not ChatGPT’s responses.
Dr Manjith Narayanan, lead author and consultant in paediatric pulmonology at the Royal Hospital for Children and Young People, Edinburgh, and honorary senior clinical lecturer at the University of Edinburgh, UK, said they did not find any obvious instances of ‘hallucinations’ – the term for false information provided by LLMs – in the responses.
‘Even though… we did not see any instance of hallucination… we need to be aware of this possibility and build mitigations against this,’ he said.
The research team plan to test LLMs against more senior doctors and investigate newer and more advanced versions of the technology.
Commenting on the findings, Professor Hilary Pinnock, ERS Education Council chair and professor of primary care respiratory medicine at the University of Edinburgh, said the study pointed to a ‘brave new world of AI-supported care’.
She added: ‘As the researchers have demonstrated, AI holds out the promise of a new way of working, but we need extensive testing of clinical accuracy and safety, pragmatic assessment of organisational efficiency, and exploration of the societal implications before we embed this technology in routine care.’
ERS Congress co-chair Professor Judith Löffler-Ragg said the research presented at this year’s event under the theme of ‘Humans and machines: getting the balance right’ was pioneering and should guide future developments.
‘It is extremely important that we view developments in technology, and specifically AI, with an open mind but also a critical eye,’ she said.
‘Our vision is to advance personalised medicine through the responsible use of AI, continuously improving respiratory medicine.’
31st July 2024
NHS staff are split on the use of artificial intelligence (AI) in healthcare, according to a new survey from the Health Foundation.
The survey, entitled ‘AI in health care: what do the public and NHS staff think?‘, asked more than 1,200 NHS staff if they currently see AI as an opportunity or a threat.
The results were ‘finely balanced’, with 44% of staff surveyed agreeing that ‘AI will mostly improve jobs in healthcare’ and 43% agreeing that ‘AI will mostly threaten healthcare jobs and professional status’.
Staff were also asked how much they agree with the statement ‘I look forward to using AI as part of my job’, with 57% agreeing and 17% disagreeing.
‘While views about the overall impact of AI on jobs in the healthcare sector might be finely balanced, staff seem much more positive when thinking about the prospect of using AI in their own role,’ the survey report states.
However, there are differences between NHS occupations in response to this question. Doctors and dentists, allied health professionals and those in scientific and technical roles were more likely to look forward to using AI. Nurses and midwives, those in administrative and clerical roles and those in other clinical services – such as healthcare assistants and healthcare support workers – were less likely to.
‘This highlights the varied perspectives of different staff groups on what AI means for healthcare, and reminds us that these technologies may have an uneven impact across the workforce, requiring tailored engagement and support,’ the report states.
This survey, took place in June and July 2024 and included 1,292 NHS staff members as well as 7,201 members of the public.
The results also revealed that three-quarters of the NHS staff surveyed (76%) said they support the use of AI for patient care, and an even greater proportion said they support the use of AI for administrative purposes (81%).
In addition, 54% of the UK public was supportive of AI being used for patient care, and 61% were in favour of its use for administrative purposes.
Around one in six members of the public (18%) and around one in 10 of the NHS staff surveyed (11%) think AI will make care quality worse, and nearly two-thirds of the NHS staff (65%) thinking AI will make them feel more distant from patients.
‘These results suggest that AI technologies will need to be designed and used in ways that protect or even enhance the human dimension of care,’ the report concludes.
Commenting on the survey results, deputy chief executive of NHS Providers, Saffron Cordery, said: ‘This data shows NHS staff support for the role AI could play in healthcare and in helping them to do their jobs. But there is also scepticism and concern amongst some patients and staff about the impact on care quality, the accuracy of decision making by AI and worries that care could become less personal.
‘It is vital that as new technologies are developed and rolled out, the needs of patients and their families are put front and centre to ensure these new services are trusted and that risks are mitigated.
‘It is also important that the opportunities presented by AI advances to transform care for patients and improve access to services do not come at the expense of critical investment in core NHS digital and IT infrastructure or in developing the skills of staff who will need to adapt to these new ways of working.’
A survey from the Microsoft company Nuance in April 2024 found over a third of patients are in favour of clinicians using AI in consultations to improve documentation processes such as clinical letters.
A version of this article was originally published by our sister publication Healthcare Leader.
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