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Take a look at a selection of our recent media coverage:
18th April 2024
Over a third of patients are in favour of clinicians using artificial intelligence (AI) in consultations to improve documentation processes such as clinical letters, according to a recent white paper from the Microsoft company Nuance.
Analysing survey responses from 13,500 participants from nine European countries plus the UK and Australia, the white paper explored patients’ recent interactions with clinicians and whether they believed AI would have helped to improve their experience.
The responses highlighted five main challenges that patients felt contributed to an unsatisfactory experience with their clinician: ineffective communication, excessive waiting times, lack of personalisation, insufficient continuity of care and limited accessibility to healthcare information.
An average of 40% of respondents felt that they didn’t receive their physician’s full attention during consultations because they were focused on their computer screens. Of those, 40% said that led to feelings of frustration. In the UK, this frustration peaked at 50% of respondents.
In order to improve this interaction, an average of 34% of respondents said they felt using AI to assist in the clinical documentation process would be a good idea, ranging from 27% in Norway to 48% in Spain.
When considering the age breakdown, respondents from younger age groups were more likely to agree AI would be beneficial. The percentage decreased with each age category from 43% of 18-to-24-year-olds to 26% of those aged 65 and over.
Although the patients surveyed had not yet had any personal experience with AI in healthcare, respondents chose freeing up time for the clinician as the most compelling reason to use AI, with an average of 45% and peaking at 55% for German respondents.
While the survey respondents broadly supported the use of AI, they also raised concerns about the use of AI in clinical settings, with 50% saying they were ‘somewhat concerned’. A further 32% were ‘not very concerned’ and 10% were ‘not concerned’.
The main cause for this concern was a lack of AI regulation at 34%, which increased to 46% in Germany and 48% in the UK. Medical information being recorded was also highlighted, with 17% recording this as a concern.
In response to this, the white paper stated: ‘The regulatory aspect of AI is changing all the time, with most governing bodies in our surveyed countries working on AI roadmaps and specific legislation.
‘Healthcare organisations should ensure they implement tools that are purpose-built for clinical environments to guarantee quality and safety, and that they clearly communicate the benefits to clinicians and patients.’
Writing in the white paper, Dr David Rhew, global chief medical officer and vice president of healthcare, at Microsoft, said: ‘With AI, we can pull together more information than ever before, extracting deeper insights into patient health and treatment options. We can accelerate and automate the workflows clinicians follow, and simplify the tasks that can draw their focus away from the patient. And we can tailor care pathways and treatments to the individual patient’s unique needs.’
The white paper also stated that ‘this total focus on the most meaningful part of their role – working directly with patients – supports clinicians’ professional satisfaction and reduces the likelihood of burnout’.
Earlier this year, the EU-funded METEOR Project highlighted widespread retention issues in Europe with 9% of doctors and nearly 14% of nurses declaring an intention to leave their profession, citing low job satisfaction, growing depersonalisation and emotional exhaustion as the primary factors.
And the recent NHS staff survey revealed that 65.56% of medical and dental staff were unable to meet all the conflicting demands on their time at work.
Previous research from Nuance in 2022 revealed that NHS healthcare professionals in acute, mental and community health settings were spending an average of 13.5 hours per week generating clinical documentation – a 25% increase since 2015.
Consultants were found to spend the longest on clinical documentation at 15.1 hours per week.
A further 3.2 hours per week were spent out-of-hours by healthcare professionals on this task, according to the research.
Some 68% of respondents said they felt it likely or very likely that their notes would be more complete if they had more time to complete them.
In an attempt to help free up doctors’ time to treat more patients and reduce waiting times, the NHS has recently announced the rollout of AI software at 10 trusts in England that aims to reduce missed appointments.
25th March 2024
A deep learning-based diagnostic tool can accurately distinguish between three of the most common brain tumours, outperforming conventional techniques, Spanish researchers report.
Glioblastoma multiforme, brain metastasis from solid tumours and primary central nervous system lymphoma accounted for up to 70% of all brain malignancies, the researchers wrote in the journal Cell Reports Medicine.
Each type of tumour required a distinct therapeutic approach, but on imaging they appeared similar, making it difficult to distinguish each type.
Corresponding study author Dr Raquel Perez-Lopez, head of Vall d’Hebron Insitute of Oncology’s (VHIO) Radiomics Group, said magnetic resonance imaging (MRI) was currently used for non-invasive differential diagnosis.
‘However, a definitive diagnosis often requires neurosurgical interventions that compromise the quality of life of patients,’ she said.
To overcome these challenges, researchers from VHIO Radiomics Group and the Neuro-Radiology Unit at Bellvitge University Hospital, developed a deep learning-based tool, leveraging spatial and temporal information from dynamic susceptibility contrast (DSC) perfusion MRI to assist in classifying brain tumours.
‘In DSC, every voxel in the image yields a unique dynamic curve that describes the temporal evolution of the T2∗-weighted signal intensity and reflects local tissue vascular properties,’ the researchers wrote.
‘The standard approach to analyse DSC is to derive metrics such as the relative cerebral blood volume (rCBV) and the percentage of signal recovery (PSR) which both simplify the dynamic signal.’
The tool, known as Diagnosis in Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), was trained to recognise characteristics of these common brain tumours using approximately 50,000 voxels from the DSC perfusion MRI images of 40 histology-confirmed patients.
It was then tested on 400 additional cases, plus an external validation cohort of 128 patients, with the researchers reporting it reached a three-way accuracy of 0.78, superior to the conventional MRI metrics of rCBV and PSR.
The tool required approximately two minutes to process a new case, the researchers said, and achieved optimal performance through training with a limited number of scans from somewhere in the order of 30 to 40 cases.
‘These data underscore the potential of DISCERN for differentiating among the three most common clinical diagnostic challenges in patients with enhancing brain lesions,’ they concluded.
Study co-author Dr Albert Pons-Escoda, a clinical neuroradiologist at Bellvitge University Hospital in Barcelona, said the work was the result of more than five years of research focused on identifying innovative magnetic resonance perfusion imaging biomarkers for differential diagnosis of brain tumors.
‘This present study integrates insights generated by other previous research projects on artificial intelligence, resulting in the development of software that automates presurgical diagnostic classification with very good precision, while facilitating its clinical applicability with a user-friendly interface for clinicians,’ he said.
AI models are increasingly being used for a range of healthcare applications. Deep learning has recently enabled scientists to accurately predict four subtypes of Parkinson’s disease based on images of patient-derived stem cells.
In a systemic review and meta-analysis published last year, Canadian researchers found that the performance of an AI model for the diagnosis of hip fractures was comparable with that of expert radiologists and surgeons.
And in research presented at the European Academy of Dermatology and Venereology Congress 2023, showed that the use of artificial intelligence software had a 100% detection rate for melanoma and saved over 1,000 face-to-face secondary care consultations during a 10-month period.
13th March 2024
Prostate tumours evolve in two distinct disease types, a new artificial intelligence (AI) study by the Pan Prostate Cancer Group has revealed, which may lead to better diagnosis and tailored treatments in future.
This international consortium of researchers, led by the universities of Oxford and Manchester, analyses genetic data from thousands of prostate cancer samples across nine countries and is aiming to develop a genetic test that, when combined with conventional staging and grading, can provide a more precise prognosis for each patient, allowing tailored treatment decisions.
For this particular study, they used AI neural networks to process data on changes in the DNA of prostate cancer samples from 159 patients in the UK.
Samples were taken after radical prostatectomy in patients with intermediate or lower risk prostate adenocarcinoma who were otherwise treatment naive.
Published in the journal Cell Genomics, the results generated an evolutionary tree that took multiple routes to two ‘evotypes’, or subgroups, of prostate cancer.
These two prostate cancer subtypes were confirmed by using two other mathematical approaches applied to different aspects of the data, as well as being validated in other independent datasets from Canada and Australia.
It is hoped that these findings could save thousands of lives in future by revolutionising how prostate cancer is diagnosed and providing tailored treatments to individual patients according to a genetic test, which will also be delivered using AI, the researchers said.
Professor Colin Cooper, professor of cancer genetics at the University of East Anglia’s Norwich Medical School, who was involved in the research, said: ‘This study is really important because until now, we thought that prostate cancer was just one type of disease. But it is only now, with advancements in artificial intelligence, that we have been able to show that there are actually two different subtypes at play.
‘We hope that the findings will not only save lives through better diagnosis [of prostate cancer] and tailored treatments in the future, but they may help researchers working in other cancer fields better understand other types of cancer too.’
Prof David Wedge, lead researcher and professor of cancer genomics and data science at the Manchester Cancer Research Centre, added: ‘This realisation is what enables us to distinguish the disease types. This hasn’t been done before because it’s more complicated than HER2+ in breast cancer, for instance.
‘This understanding is pivotal as it allows us to classify tumours based on their evolutionary trajectory rather than solely on individual gene mutations or expression patterns.’
As well as this project in prostate cancer, AI is being used in a number of new clinical studies into disease areas such as cardiovascular disease.
And neural networks in particular have previously been used, for example, in an AI study of Parkinson’s disease, which revealed four subtypes of the disease.
27th February 2024
A clinical trial at Amsterdam UMC is aiming to show that an artificial intelligence (AI) algorithm can reduce the number of blood cultures taken in emergency departments (EDs) worldwide by almost a third.
The algorithm takes data from the electronic health record of patients with suspected sepsis, such as body temperature and other standard lab results that provide information about possible infections, to identify patients with a low risk of infection or inflammation.
The clinician uses this prediction, together with their own observation, to then consider whether a blood culture is required.
Dr Prabath Nanayakkara, professor of internal medicine at Amsterdam UMC, whose work focuses on increasing efficiency in acute care, said: ‘Of all the blood cultures that are done in the emergency room, only 15% indicate that something is wrong, and it’s even more interesting if you take a closer look at those positive results. Then about half of the positive blood cultures turn out to be false positives. We’re used to doing it this way but that doesn’t mean the analysis is necessary. Certainly not when you look at the numbers.’
The new clinical trial – known as the ABC study – will test the algorithm in practice and is one of the first randomised controlled trials worldwide to consider such an algorithm in acute care.
It was previously developed by Amsterdam UMC’s acute AI team and extensively tested and validated nationally and internationally, the team said.
The AI’s prediction is now built into the electronic patient record at Amsterdam UMC, so the healthcare provider is shown this prediction on the computer screen when requesting a blood culture in the intervention group, which the researchers say is a ‘unique’ approach.
‘With tailor-made advice for each patient, we expect to drastically reduce unnecessary cultures, hopefully by up to 30%,’ said Professor Nanayakkara.
‘If this study is successful, we will not only reduce the number of blood cultures – and therefore the costs – but also ensure that the number of false positive blood tests decreases and thus, no patients will be treated unnecessarily.
‘False positive results lead to uncertainty among patients and doctors, extra procedures, excessive treatment with antibiotics and, longer hospital admissions and even higher mortality in that group.’
He added: ‘We shouldn’t do things in healthcare that aren’t necessary [and] this is pre-eminently an issue in which AI can be used. In doing so, we are showing that AI can really change healthcare.’
In October 2023, a study presented at the European Society of Emergency Medicine congress led to calls for increased sepsis awareness as two of the four internationally recommended sepsis screening tools used by emergency medical services were deemed inadequate for recognising the condition.
And in February 2024, updated guidance from the National Institute for Health and Care Excellence on identifying and managing sepsis in over-16s recommended better targeting of antibiotics for suspected sepsis.
16th February 2024
A new international project aiming to use artificial intelligence (AI) and genomics data to personalise therapies for patients with cardiovascular disease (CVD) has been announced.
The Next Generation Tools for Genome-Centric Multimodal Data Integration in Personalised Cardiovascular Medicine (NextGen) project aims to build AI-supported novel and synergistic tools to enable portable multimodal, multiomic and clinically oriented research in high-impact areas of cardiovascular medicine.
The tools will benefit researchers, innovators and healthcare professionals by identifying and overcoming health data linkage barriers in exemplar cardiovascular use cases that are complex or intractable with existing technology.
The ultimate goal is to provide faster diagnosis and better, more personalised treatments for patients while capitalising on increasing innovations and trends in AI technology.
The NextGen project will be delivered by a 21-member consortium of academic, clinical, technical and commercial partners from across Europe and the US, including the European Society for Cardiology, and led by University Medical Center (UMC) Utrecht and Queen Mary University of London.
Project coordinator Professor Pim van der Harst, interventional cardiologist and head of the department of cardiology at the UMC Utrecht, said: ‘No two people are exactly the same, and so it makes sense that each person needs a slightly different strategy to optimise their health. Personalised medicine is, therefore, the way forward for preventing heart disease, speeding up diagnosis, and monitoring and treating people with CVD.
‘To develop individualised therapies, we need to compile as much information as possible about individuals, and that’s where NextGen comes in. The unique picture we generate will then form the basis for improving cardiovascular health and wellbeing.’
Several real-world pilots will demonstrate the effectiveness of NextGen tools and will be integrated in the NextGen Pathfinder network of five collaborating clinical sites as a self-contained data ecosystem and comprehensive proof of concept.
The work will complement the ‘1+ Million Genomes’ initiative, which aims to enable secure access to genomics and clinical data across Europe, and the European Health Data Space – a European Commission governance framework for the safe and secure exchange, use and reuse of health data.
Consortium member Professor Panos Deloukas, professor of cardiovascular genomics and dean for Life Sciences at Queen Mary University of London’s William Harvey Research Institute, added: ‘This is a tremendous opportunity and a challenge we have in building the right toolbox that will allow [us] to unite CVD patient data across Europe and implement precision medicine to improve cardiovascular healthcare.’
The NextGen project has received €7.6 million from the EU’s Horizon Europe programme.
In August 2023, a genetic study revealed how the use of clopidogrel in British patients of south Asian ancestry appears to be less effective at preventing recurrent myocardial infarction than in those of European descent.
And earlier in 2023, single cell and spatial genomics combined with computational techniques were used to develop a comprehensive Heart Cell Atlas to better understand the heart and how it responds to treatments.
4th January 2024
With the results of the lung cancer screening SUMMIT study expected imminently, Helen Gilbert caught up with consultant respiratory physician Dr Neal Navani to discuss this research, promising new innovations in lung cancer diagnostics and what they might mean for the future of lung cancer care.
As Cancer Research UK’s Lung Cancer Centre of Excellence, University College London and University College London Hospital (UCLH) have been at the forefront of lung cancer innovations, pioneering diagnostic modalities such as endobronchial ultrasound.
This diagnostic focus is particularly pertinent as lung cancer is Europe’s biggest cancer killer, with 380,000 deaths across the continent in 2020 – a fifth of all cancer deaths.
In England, more than 60% of lung cancer patients are diagnosed at either stage three or four, and this late diagnosis is a frustration for Dr Neal Navani, lead consultant respiratory physician for lung cancer services at UCLH, as he says cure rates can be as high as 80-90% for patients whose small, early-stage lung cancer is detected.
Dr Navani, who is also the clinical lead of the UK National Lung Cancer Audit and clinical director for the Centre for Cancer Outcomes at the North Central London Cancer Alliance, has long been involved in pioneering research at UCLH to improve early detection and diagnosis.
And recent projects suggest there are further innovations on the horizon that have the potential to improve patient outcomes.
In May 2023, the largest lung cancer screening study of its kind in the UK drew to a close.
The four-and-a-half-year SUMMIT study was a collaboration between researchers from UCLH, University College London (UCL), the National Institute for Health Research, UCLH Biomedical Research Centre and GRAIL – a US healthcare company focused on the early detection of cancer.
Their aim was to identify lung cancer early among at-risk Londoners and support the development of a new blood test for the early detection of lung and multiple cancer types.
More than 13,000 people aged 55-77 from north and east London who had a significant smoking history were offered a blood test and a low-dose CT scan of their lungs. They were followed up at three months or immediately if a cause for concern was identified.
Dr Navani describes the research – results of which are expected imminently – as ‘a really fantastic, rich data set on which we can look to answer a lot of questions about detecting cancer early’.
He is particularly interested in developing a model that incorporates PET-CT scans to predict malignancy in screen-detected lung nodules. Often these appear like freckles on the lung, which may or may not be cancerous.
The challenge, he says, is working out whether they are malignant or benign, and currently this is done using a risk calculator developed in 2005.
It involves an injection of radioactive sugar before a PET-CT scan to see whether the nodule – or anything else for that matter – takes up the sugar. This then correlates with the risk of malignancy.
However, Dr Navani describes the current tool, which was developed in 2005, as out of date and prone to underestimating the risk of cancer in lung nodules.
Data from the SUMMIT trial are set to be used to develop and test a new risk calculator that takes into account more than 10 factors including family history, smoking and the size and appearance of nodules. It aims to accurately predict the chance of a nodule being cancerous.
‘We’re able to see whether sugar is taken up by that nodule in the lung – the idea being that small cancers use up more sugar than nodules that are not due to cancer,’ Dr Navani says.
‘Data for that work are being collected and developed. We’re pulling together data through other trials doing a similar thing and hopefully we’ll be able to clarify the role of PET-CT scanning for nodules in the next two years.’
The risk calculator will be compared against the existing model as well as others that do not include PET-CT scanning.
If found to be more accurate, the potential benefits are numerous and may include fewer patient investigations at lower cost, earlier treatment and reduced anxiety for those called in, Dr Navani explains.
UCL researchers are also using blood samples from the SUMMIT study to evaluate a blood test that can diagnose tumours earlier and detect 50 types of cancer, including lung cancer, with high accuracy.
Developed by GRAIL and an international team of researchers co-led by UCL, the test looks for tell-tale chemical changes to bits of genetic code – cell-free DNA – that leak from tumours into the bloodstream.
It was developed using artificial intelligence (AI) after researchers fed data on methylation patterns from the blood samples of thousands of cancer patients into a machine learning algorithm. It is said to identify many types of cancer, including bowel, ovarian and pancreatic, and can diagnose in which tissue the cancer originated with 96% accuracy.
But the potential of technology in bolstering cancer diagnosis doesn’t stop at AI. Another promising area of innovation is robotics.
Dr Navani is intrigued by the potential of this kind of diagnostic ability, and he is aware of robotic techniques that will be ‘the subject of research over the next year or two’.
He says: ‘We need to understand the cost effectiveness of robotic diagnosis of lung nodules. It’s potentially exciting.’
Earlier this year NHS clinicians at the Royal Brompton and St Bartholomew’s Hospital in London began a clinical study trialling a robotic-assisted bronchoscopy system.
Each hospital site is aiming to recruit around 50 patients with small lung nodules located in areas that are challenging to reach via traditional bronchoscopy.
The system combines software, robotic assistance and a flexible catheter with a camera to create a 3D roadmap of the lungs – much like a car’s sat-nav.
Doctors are directed to deep and hard-to-reach areas in each of the 18 segments of the lung, with the aim of removing tissue samples for biopsy with greater precision and accuracy.
The benefits of diagnosing a lung nodule accurately with a tiny camera could ‘open up a world of possibilities in terms of drug delivery, or ablation [to destroy cancerous nodules] in a controlled and accurate way,’ says Dr Navani. ‘I think in the next five to 10 years we’re going to see novel diagnosis and treatment options for our patients with early-stage lung cancer in particular.’
Another key development Dr Navani anticipates is the continued and increasing importance of collaboration, particularly when it comes to technology.
Endobronchial ultrasound (EBUS), one of the biggest innovations in respiratory medicine over the last 15 years, evolved from endoscopic ultrasound used in other clinical areas.
EBUS was trialled in the early 2000s by the UCLH research team, of which Dr Navani was a leading player, and uses a bronchoscope with a light, camera and integral ultrasound scanner to produce a detailed image inside the chest.
It enables doctors to take targeted needle biopsies of any enlarged lymph nodes and suspicious lesions while avoiding areas such as blood vessels.
Prior to this, patients at-risk of lung conditions required incisions to the chest under general anaesthetic, resulting in hospital stays and the possibility of complications or even death.
The arrival of EBUS in clinical practice in 2007 meant the diagnostic procedure could be performed on outpatients in under 30 minutes, with patients able to leave just one or two hours later.
‘It’s a very safe technique [and] in the last 10-15 years it’s really become a mainstay of diagnosis in respiratory medicine,’ Dr Navani acknowledges. ‘It started off very slowly but now in the UK there are 140 centres that are doing this technique and it’s been adopted globally for diagnosing lung conditions.’
Dr Navani believes the adaption of tools and techniques used in other clinical fields will continue to play a pivotal role in the advancement of lung cancer diagnostics and treatment. He points out, for example, that tumour ablation, which is used to treat lung and liver cancer, is now happening at a research stage for pancreatic cancer.
And this collaboration doesn’t just extend across clinical specialities. Imaging and information providers, including the likes of Fujifilm, also serve a vital purpose by providing increasingly innovative imaging solutions.
In June 2023, NHS England announced the national rollout of a targeted lung cancer screening programme to help detect cancer sooner and speed up diagnosis.
The rollout followed a successful pilot phase in which lung cancer scanning trucks carrying out on-the-spot chest scans operated from convenient locations such as football stadiums, supermarket car parks and town centres.
In September, NHS England announced that more than one million people had been invited for a lung cancer check via the scheme and almost 2,400 cancers had been caught – an impressive 76% of which were diagnosed at stage one or two.
‘That’s going to hopefully need innovative imaging solutions, particularly low-dose scanners, and I think we need to work with industry in terms of the use of artificial intelligence to help with the reporting of those scans,’ Dr Navani says.
Innovative diagnostic imaging techniques are certainly in development, and Dr Navani sees huge potential in new technologies for treating patients, too.
‘In terms of delivering novel therapies, in the future there may be a role for delivering drugs directly into the lungs, the pleural space or endobronchially, lymph nodes, or primary lung lesions,’ he says.
Dr Navani describes working in a hospital that is attached to a world-class university as ‘fantastic’ because it grants access to ‘extraordinary expertise’ spanning science, sociology, data science, computer science and engineering.
‘The research into lung cancer at UCL is really incredibly broad and, dare I say it, world leading, right the way through the basic science, biology and understanding how cancer develops and spreads and changes over time… to understanding the societal impact, equality and equity of care,’ he says.
According to Dr Navani, there appears to be a big difference in the outcomes of lung cancer patients based on socio-economic status.
‘We’ve really tried to address this in the National Cancer Audit, but it remains a significant challenge,’ he says. “A lot of this comes down to local resources… access to healthcare, equality and subsequent diagnosis and treatment in a timely fashion.’
Another major unmet need, Dr Navani says, is the 15% of patients with lung cancer who have never smoked and it’s here that ‘urgent research is needed’.
‘Given the high burden of lung cancer care, that’s a significant number of people – if you consider [non-smoking-related lung cancer] as a cancer in its own right it would be the seventh most common cause of cancer death,’ he says.
‘We’re really starting to get to grips with lung cancer in smokers but we are still at the early stages of understanding why people who’ve never smoked develop lung cancer. It would be important to predict who these people might be so that we can identify them at an earlier stage so hopefully their outcome will be better.’
The most pressing issue facing the NHS is limited resources, according to Dr Navani.
‘We simply don’t have enough scanners, radiologists, or space to do bronchoscopies,’ he states. ‘We’ve talked a lot about innovation but actually the most important thing that can be done to improve lung cancer care is for each hospital and primary care setting to have the appropriate resources to deliver what we know is already appropriate care, to drive out inequalities and drive everybody up to the best possible standards.’
While the future of funding for lung cancer care in the UK remains in flux, one thing is for certain: the research, expertise and drive to support the early diagnosis of patients remains, and Dr Navani’s commitment to supporting patients through innovative routes is stronger than ever.
13th October 2023
The use of artificial intelligence (AI) software has shown a 100% detection rate for melanoma and saved over 1,000 face-to-face secondary care consultations during a 10-month period, according to a study presented at the recent European Academy of Dermatology and Venereology (EADV) Congress 2023.
The study was able to able to correctly detect all 59 cases of suspected melanoma between April 2022 and January 2023, as well as 99.5% of all skin cancers (189/190 cases) and 92.5% of pre-cancerous lesions (541/585).
The software assessed 22,356 patients with suspected skin cancers using three versions of an AI software. The first version tested in 2020-21 had an 85.9% detection rate for melanoma (195/227), 83.8% for all skin cancer (903/1,078) and 54.1% for pre-cancerous lesions (496/917).
Lead author Dr Kashini Andrew, a specialist registrar at University Hospitals Birmingham NHS Foundation Trust, commented: ‘The latest version of the software has saved over 1,000 face-to-face consultations in the secondary care setting between April 2022 and January 2023, freeing up more time for patients that need urgent attention.‘
The research team noted that the data is ‘incredibly encouraging‘, however, co-author and colleague, Dr Irshad Zaki, consultant dermatologist, said: ‘We would like to stress that AI should not be used as a standalone tool in skin cancer detection and that AI is not a substitute for consultant dermatologists.‘
Evidence of the need for appropriate clinical oversight was shown among the basal cell carcinoma cases as a single case was missed by the AI tool and later identified at a second read by what the researchers termed ‘a dermatologist “safety net“‘.
Dr Kashini Andrew added: ‘This study has demonstrated how AI is rapidly improving and learning, with the high accuracy directly attributable to improvements in AI training techniques and the quality of data used to train the AI.
‘The role of AI in dermatology and the most appropriate pathway are debated. Further research with appropriate clinical oversight may allow the deployment of AI as a triage tool. However, any pathway must demonstrate cost-effectiveness, and AI is currently not a standalone tool in dermatology. Our data shows the great promise of AI in future provision of healthcare.’
This supports the findings of previous studies including a 2022 systematic review in which the researchers concluded that ‘the performance of artificial intelligence in melanoma is satisfactory and the future for potential applications is enormous‘.
9th October 2023
Almost two thirds of patients are comfortable with using healthcare settings that use artificial intelligence (AI), but only if they are familiar with the technology, according to a new survey from GlobalData.
The results revealed that 60% of patients who were familiar with AI were either very or quite comfortable with attending an AI-enabled healthcare setting. When it came to people who weren’t familiar with the technology, this level of comfort fell to just 7% of patients.
A lack of in-person interaction was the top patient concern associated with physicians using AI in clinical practice, and most patients felt more comfortable with physicians using it to automate administrative tasks compared to directing patient care.
Faster healthcare delivery and mitigation of healthcare staff shortages were identified as the main benefits associated with AI use in clinical practice.
The survey data also reveals that those aged 18-55 years were more likely to be familiar with AI than those aged 56 years and over, with more than 50% of the younger group rating their knowledge as moderately or very familiar.
Commenting on the important and evolving use of AI to detect image-based diseases such as cancer, Urte Jakimaviciute, senior director of market research at GlobalData, said: ‘Together with the development of a robust regulatory framework, it is imperative to prioritise patient education regarding the technology.
‘This education should aim to enhance comprehension of AI’s utilisation, its potential advantages, and associated adoption risks, ultimately fostering increased trust in AI. Enhanced knowledge empowers individuals to make informed decisions and mitigate biases linked to this technology.’
Similar issues around trust have been identified in previous studies. For example, a 2021 study looking at patient apprehensions about the use of AI in healthcare, published in the journal NPJ Digital Medicine, identified concerns relating to its safety, threats to patient choice, potential increases in healthcare costs, data-source bias and data security.
These authors also concluded that ‘patient acceptance of AI is contingent on mitigating these possible harms’.
The new GlobalData survey, Thematic Intelligence: AI in Clinical Practice – Patient Perspective 2023, saw 574 patient respondents from the US, France, Germany, Italy, Spain, the UK, Japan, Brazil, Canada, India and Mexico.
The patients were diagnosed with conditions such as heart diseases, diabetes, multiple sclerosis, cancer, chronic respiratory conditions, rheumatoid arthritis, psoriasis and inflammatory bowel disease. They were surveyed between July and August 2023.
24th August 2023
Artificial intelligence technologies could be utilised to reduce the time needed for contouring during radiotherapy treatment planning, according to draft guidance from NICE.
In June 2023, NICE guidance suggested lower intensity and shorter duration radiotherapy in breast cancer did not impact on breast cancer-related mortality or disease recurrence and therefore served as a suitable alternative to the current standard care.
Now, related draft guidance proposes that artificial intelligence (AI) technologies have the potential to help healthcare professionals to produce contours more quickly, which could improve workflow efficiency.
Evidence made available to NICE’s independent medical technologies advisory committee indicates how AI technologies generally produce similar quality contours of organs at risk as those carried out manually, with most only needing minor edits.
Currently, following a CT or MRI scan, a radiographer has to manually contour an image to highlight organs at risk of radiation damage, lymph nodes and the site of the cancer. The dose of radiotherapy is not only calculated to target the tumour site but also to prevent organs and healthy tissue from being damaged.
Clinical experts advising the independent NICE committee estimated a time saving of 10-30 minutes per plan, depending on the amount of editing needed, while the clinical evidence presented to the committee suggests it may range between three and 80 minutes of time saved per plan.
Sarah Byron, programme director for health technologies at NICE, said: ‘NHS colleagues working on the front line in radiotherapy departments are under severe pressure with thousands of people waiting for scans.
‘The role imaging plays in radiotherapy treatment planning is quite pivotal, so recommending the use of AI technologies to help support treatment planning alongside clinical oversight by a trained healthcare professional could save both time and money.‘
Health and social care secretary Steve Barclay added: ‘It’s hugely encouraging to see the first positive recommendation for AI technologies from a NICE committee, as I’ve been clear the NHS must embrace innovation to keep fit for the future.
‘These tools have the potential to improve efficiency and save clinicians’ thousands of hours of time that can be spent on patient care. Smart use of tech is a key part of our NHS Long Term Workforce Plan, and we’re establishing an expert group to work through what skills and training NHS staff may need to make best use of AI.‘
30th May 2023
Heart attack diagnosis could be achieved quicker and more accurately via the use of a new artificial intelligence algorithm developed by the University of Edinburgh.
Tested on 10,286 patients in six countries, the algorithm – named CoDE-ACS – was able to rule out a heart attack in more than double the number of patients compared to guideline-recommended pathways, and with an accuracy of 99.6%.
The tool was also found to identify those whose abnormal troponin levels were due to a heart attack rather than another condition.
It performed well regardless of age, sex or pre-existing health condition, which the authors say reduces the potential for misdiagnosis and inequalities across the population.
Funded by the British Heart Foundation (BHF) and the UK’s National Institute for Health and Care Research, the study was published in the journal Nature Medicine.
Its lead author, Professor Nicholas Mills, BHF professor of cardiology at the Centre for Cardiovascular Science, University of Edinburgh, said: ‘For patients with acute chest pain due to a heart attack, early diagnosis and treatment saves lives. Unfortunately, many conditions cause these common symptoms, and the diagnosis is not always straight forward. Harnessing data and artificial intelligence to support clinical decisions has enormous potential to improve care for patients and efficiency in our busy emergency departments.’
CODE-ACS was developed using data from 10,038 patients in Scotland who had arrived at hospital with a suspected heart attack. It uses routinely collected patient information, such as age, sex, ECG findings and medical history, as well as troponin levels, to predict the probability that an individual has had a heart attack. The result is a probability score from 0 to 100 for each patient.
Clinical trials are now underway in Scotland to assess whether the AI tool can help doctors reduce pressure on overcrowded emergency departments.
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