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Digital heart screening can improve health, shows study

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The Healthy Heart study – a study investigating the benefits of digital at-home heart health screening – has revealed how digital screening can lead to improved health.

The study revealed that a digital-first approach to screening heart health at home could increase the number of patients testing for cardiovascular disease (CVD), as well as spotting, and acting on high cholesterol, by up to 57 per cent.

The Healthy Heart Study, conducted by Pharmacy2U and PocDoc., saw 3,871 people across the UK sign up to a digital-first heart health screening process. Participants received PocDoc’s ‘Healthy Heart Check’ on day one and day 56 and were offered two clinical reviews with a Pharmacy2U pharmacist upon completion of each test.

Over two thirds of eligible people invited to complete their first test did so, notably higher than the 44% who attended an invitation to their NHS Health Check in the past five years.

Over a quarter of those who completed their digital home health check had never had their cholesterol levels checked before, suggesting that a digital-first pathway can increase access to lifesaving preventative measures.

This is a particularly pertinent issue for those in under-privileged communities. Research has found that individuals from deprived backgrounds are less likely to attend their NHS Health Check, despite facing a higher risk of heart disease. Almost a third (30%) of participants in the Healthy Heart Study were from backgrounds categorised as ‘deprived’.

“Despite significant advances in treatment and prevention of heart conditions in recent years, cardiovascular disease still places a substantial burden on public health and the healthcare system”, said Kevin Heath, CEO of Pharmacy2U.

“The NHS Long Term Plan (2019) identified CVD as the single biggest area where lives can be saved over the next decade, while a digital-first screening programme, delivered by pharmacies, ticks all three of the government’s aims to focus on prevention, delivered digitally, within the primary care sector. This study shows just how effective that approach could be, were the NHS to invest in a nationwide rollout.”

Cholesterol and heart health checks are currently part of the NHS Health Check offered to all individuals aged over 40 in England. However, more than half of those eligible have not attended on in the past five years. Providing this service digitally could not only increase the number of people getting screened but also relieve significant pressure on busy GP practices.

The Healthy Heart Study uncovered a wide range of positive outcomes for participants. Three-quarters said they were inclined to make more healthy choices after being involved, while 46 per cent of those who completed the end-of-study survey self-reported an improvement in their cholesterol after 60 days of online advice.

Furthermore, participants reported satisfaction with PocDoc’s digital-first approach. 95 per cent said the app download process was “easy” or “very easy”; 86 per cent found interpreting results “easy” or “very easy”; and 72 per cent agreed the process was more convenient than visiting a GP. A resounding nine in 10 said the NHS should offer the service more widely.

Steve Roest, CEO of PocDoc, said: “The Healthy Heart Study is the largest of its kind in the UK, leading the way in digital-first pathways for Cardiovascular Disease prevention. The findings are crystal clear – using digital technology platforms like PocDoc and Pharmacy2u can deliver an efficient, high-quality prevention pathway that reaches people otherwise not engaging with healthcare.”

Heath said: “Digital-first healthcare puts people in control of their health by empowering individuals from all walks of life to measure, monitor, and manage their symptoms from the palm of their hand. Something we need now more than ever, not least to relieve the pressure on our hospitals and GP surgeries.”

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AI system could help identify Alzheimer’s earlier

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An AI tool could help identify Alzheimer’s disease around two years earlier by analysing signals already recorded in patients’ clinical records.

DementAI, a prototype developed by consultancy Katalyze Data, analyses existing medical record data to flag patients who may show early signs of the condition but have not yet been referred for specialist assessment.

Built as an end-to-end working prototype, the system connects stages clinicians often manage separately, from analysing medical records to applying models within decision pathways.

It is designed to work using information healthcare providers already hold, turning fragmented data into actionable insight without adding new screening burdens.

The system combines structured medical records, brain activity data and unstructured clinical information, using synthetic data where appropriate to support development.

By blending these signals, it aims to detect subtle patterns of decline that may be difficult to identify during short consultations.

Tamás Bosznay, principal consultant at Katalyze Data, said: “We are in a race against time when it comes to dementia.

“Early identification can make a meaningful difference to how patients and families experience the condition.

“But without better ways of finding people sooner, those opportunities can be lost.

“We didn’t build DementAI just to make predictions; we built it to buy patients time.

“By surfacing the signals already hiding in plain sight within clinical records, the system is designed to help ensure that when care teams are ready to act, the right patients are identified earlier and more consistently.”

DementAI was developed as part of the SAS Hackathon 2025, where it won the healthcare and life sciences category.

The team is now seeking engagement with NHS trusts to explore pilot deployments that could validate the model’s impact and support efforts to reduce delays in diagnosis.

Dr Iain Brown, global head of AI and data science at SAS, said: “Synthetic data, agentic AI concepts and governance are not ‘nice-to-haves’ in sensitive settings like healthcare.

“They are what make innovation usable at scale.

“DementAI shows how artificial intelligence can be applied in a way that is both ambitious and responsible.

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Smart lights linked to fewer care home falls

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AI smart lights in care homes were linked to up to 75 per cent fewer hospital visits after falls, according to an NHS evaluation.

The study examined 87 rooms across seven care homes providing residential, nursing, dementia and assisted living care.

Researchers compared six months of baseline data with six months after installing Nobi Smart Lights, AI-enabled ceiling-mounted devices designed to detect falls and alert staff within seconds.

The lights also turn on automatically when residents get out of bed, helping reduce the risk of night-time falls. Some homes reported zero fall-related hospital admissions during the evaluation period, while ambulance call-outs fell by up to 65 per cent.

Staff reported greater confidence when responding to unwitnessed incidents and said they spent less time reconstructing events or completing documentation.

Better visibility also helped staff distinguish genuine falls from controlled descents, where someone lowers themselves to the floor intentionally or slowly, allowing more incidents to be managed safely inside the care home.

The evaluation was carried out by the Suffolk and North East Essex Integrated Care Board.

“The Nobi light gives me peace of mind because Mum does fall a lot,” said the daughter of a resident at a participating Suffolk care home.

“I felt guilty about her going into a home, but now I know staff are alerted instantly and can be there straight away.”

The work formed part of the Integrated Care Board’s Digitising Social Care Programme, which supports care providers to adopt digital tools.

Implementation was delivered in partnership with Porters Care, one of Nobi’s UK partners, with support from Suffolk County Council and participating care providers.

Using NHS reference costs, the evaluation estimated £89,000 in avoided emergency care costs over six months, equivalent to a projected return on investment of around 196 per cent over three years.

Roeland Pilgrims, chief executive and co-founder of Nobi, said: “This independent NHS evaluation shows how intelligent care technology can deliver measurable improvements for residents, care teams and the wider health system.

“By giving staff timely, reliable insight, we can help reduce avoidable hospital admissions while improving safety, dignity and peace of mind.”

David Knowles, managing director of Porters Care, added: “These findings show the real-world impact of smart technology in care homes.

“By improving how falls are detected and understood, Nobi helps teams make clearer decisions and avoid unnecessary hospital admissions, while keeping residents safe.”

Further independent NHS-led evaluations are underway in other regions of the UK.

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AI cancer tools may rely on diagnostic shortcuts

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Some AI cancer tools analysing tumour images may rely on visual shortcuts rather than genuine biological signals, according to new research.

A large analysis of more than 8,000 patient samples across four cancer types found artificial intelligence models often achieved high accuracy by relying on statistical correlations rather than true biological signals.

The findings raise concerns that some AI pathology tools, designed to help identify cancer faster and potentially reduce testing costs, may not yet be reliable enough for routine clinical care.

The study compared the performance of machine learning systems across breast, colorectal, lung and endometrial cancers.

Instead of detecting specific genetic mutations directly, some models appeared to rely on related clinical features.

For example, rather than identifying mutations in the cancer-related BRAF gene, a model might detect a linked feature called microsatellite instability, a condition where the cell’s DNA repair system does not function properly.

Because these features often occur together, the system may predict BRAF mutation status using that association. This means predictions may only remain accurate when both features appear together.

“It’s a bit like judging a restaurant’s quality by the queue of people waiting to get in: it’s a useful shortcut, but it’s not a direct measure of what’s happening in the kitchen,” said Dr Fayyaz Minhas, associate professor and lead author of the study at Warwick.

“Many AI pathology models are doing the same thing, relying on correlations between biomarkers or on obvious tissue features, rather than isolating biomarker-specific signals. And when conditions change, these shortcuts often fall apart.”

When researchers tested the models within specific patient subgroups, such as only high-grade breast cancers or tumours with microsatellite instability, accuracy fell substantially.

For some prediction tasks, the advantage of deep learning over existing clinical information was limited.

AI systems achieved accuracy scores of just over 80 per cent when predicting biomarkers, compared with around 75 per cent using tumour grade alone, a measure already assessed by pathologists.

Kim Branson, senior vice president and global head of artificial intelligence and machine learning at GSK and co-author of the study, said: “We’ve found that predicting a BRAF mutation by looking at correlated features like MSI is often like predicting rain by looking at umbrellas it works, but it doesn’t mean you understand meteorology.

“Crucially, if a model cannot demonstrate information gain above a simple pathologist-assigned grade, we haven’t advanced the field; we’ve just automated a shortcut.”

The researchers said machine learning could still prove useful for research, drug development screening and clinical decision support.

However, they argue future AI systems should move beyond correlation-based learning and instead model underlying biological relationships.

Dr Minhas added: “This research is not a condemnation of AI in pathology. It is a wake-up call.

“Current models may perform well in controlled settings but rely on statistical shortcuts rather than genuine biological understanding.

“Until more robust evaluation standards are in place, these tools should not be seen as replacements for molecular testing, and it is essential that clinicians and researchers understand their limitations and use them with appropriate caution.”

Professor Nasir Rajpoot, director of the Tissue Image Analytics Centre at the University of Warwick, said: “This study highlights a critical point about the rollout of AI in medicine: to deliver real and lasting impact, the value of AI-based clinically important predictions must be judged through rigorous, bias-aware evaluation, rather than relying solely on headline accuracies that fail to account for confounding effects.”

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