AI can be good for our HEALTH and WELLBEING
"If we get things right, the possibilities for AI to transform health and medicine are endless. It can be of massive public benefit. But more than that, it has to be."
Professors Andres Floto, Mihaela van der Schaar and Eoin McKinney, Cambridge Centre for AI in Medicine
Cambridge researchers are looking at ways that AI can transform everything from drug discovery to Alzheimer's diagnoses to GP consultations.
Tackling dementia
In 2024, Professor Zoe Kourtzi in the Department of Psychology showed that an AI tool developed by her team could outperform clinical tests at predicting whether people with early signs of dementia will remain stable or develop Alzheimer’s disease.
At a time of intense pressure on the NHS, tools such as this could help doctors prioritise care for those patients who need it most, while removing the need for invasive and costly diagnostic tests for those whose condition will remain stable. They can also give patients peace of mind that their condition is unlikely to worsen, or, for those less fortunate, it can help them and their families prepare.
These tools could also be transformational in the search for new drugs, making clinical trials more effective, faster and cheaper, says Kourtzi.
Recently, two dementia drugs – lecanemab and donanemab – have shown promise in slowing the disease, but the benefits compared to the costs were judged insufficient to warrant approval for use within the NHS. Beyond these, there’s been limited progress in the field.
Part of the problem is that clinical trials often focus on the wrong people, which is where AI may help to better decide who to include in trials.
“If you have people that the AI models say will not develop pathology, you won't want to put them in your trial. They'll only mess up the statistics, and then [the trials] will never show an effect, no matter if you have the best drug in the world. And if you include people who will progress really fast, it might be already too late for the drug to show benefit.”
Kourtzi is leading one of ai@cam’s AI-deas projects to create a ‘BrainHealth hub’ to tackle the global brain and mental health crisis. It will bridge the gap between engineers, mathematicians and computer scientists who have the tools but lack the data, and clinicians and neuroscientists who have the data but lack advanced tools to mine them.
“Our idea is to create a ‘hothouse’ of ideas where people can come together to ask and answer challenging questions.“
University researchers, industry partners, the charity sector and policymakers will explore questions such as: how can we use AI for drug discovery, to accelerate clinical trials and develop new treatments, and how can we build interpretable AI models that can be translated to clinical tools?”
The need for such AI to be reliable and responsible is a theme that comes up frequently when Kourtzi speaks to patient groups.
“When doctors are using a complex diagnostic tool like an MRI machine, patients don't query whether they understand what's in this machine, why it works this way. What they want to know is that it's gone through regulatory standards, it's safe to use and can be trusted. It’s exactly the same with AI.”
Making GP practices more efficient
Professor Niels Peek from The Healthcare Improvement Studies (THIS) Institute believes that AI could have a major impact on primary care services, such as GP practices, by tackling some of their most mundane tasks.
One such application involves the use of ‘digital scribes’ to record, transcribe, and summarise conversations between GPs and patients.
“If you look at the amount of time that clinicians spend on that type of work, it’s just incredible,” he says.
“Considering that clinician time is probably the most precious commodity within the NHS, this is technology that could be transformational.”
It is likely that the NHS will increasingly adopt digital scribe technology in the future, so it will be important to ensure the summaries are accurate and do not omit key points or add things that were not mentioned (a ‘hallucination’). With support from The Health Foundation, Peek is asking whether the technology actually saves time? “If you have to spend a lot of time correcting its outputs, then it's no longer a given that it actually does save you time.”
Peek believes that in the future, every clinical consultation will be recorded digitally, stored as part of a patient's record, and summarised with AI. But the existing technology environment, particularly in primary care, presents a challenge.
“GPs use electronic health records that have evolved over time and often look outdated. Any new technology must fit within these systems. Asking people to log into a different system is not feasible.”
Peek is also involved in evaluating Patchs, a tool that applies AI to the process of booking GP appointments and conducting online consultations. It was designed by GP staff and patients, in collaboration with The University of Manchester (where Peek was formerly based) and commercialised by the company Patchs Health. It is now used by around one in 10 GP practices across England.
Working with end users – patients, GPs, and particularly the administrative staff who use these systems on a day-to-day basis – is crucial. “You have to make sure they fit both with the systems people are already using, and also with how they do things, with their workflows. Only then will you see differences that translate into real benefits to people.”
Addressing mental health amoung young people
Over recent years, there has been a significant increase in the prevalence of mental health disorders among young people. But with stretched NHS resources, it can be difficult to access Child and Adolescent Mental Health Services (CAMHS).
Not every child recommended for a referral will need to see a mental health specialist, says Dr Anna Moore from the Department of Psychiatry, but the bottleneck means they can be on the waiting list for up to two years only to be told they don’t meet the criteria for treatment. The quality of advice they get about alternative options that do meet their needs varies a lot.
Moore is interested in whether AI can help manage this bottleneck by identifying those children in greatest need for support, and helping those who don’t need specialist CAMHS to find suitable support from elsewhere. One way to do so is by using data collected routinely on children.
“The kinds of data that help us do this can be some of the really sensitive data about people,” she says. “It might be health information, how they're doing at school, but it could also be information such as they got drunk last weekend and ended up in A&E.”
For this reason, she says, it’s essential that they work closely with members of the public when designing such a system to make sure people understand what they are doing, the kinds of data they are considering using and how it might be used, but also how it might improve the care of young people with mental health problems.
One of the questions that often comes up from ethicists is whether, given the difficulties in accessing CAMHS, it is necessarily a good thing to identify children if they cannot then access services.
“Yes, we can identify those kids who need help, but we need to ask, ‘but so what?’,” she says. The tool will need to suggest a referral to CAMHS for the children who need it, but for those who have a problem but could be better supported in other ways than CAMHS that could be more flexible to their needs, can it signpost them to helpful, evidence-based, age-appropriate information?
Moore is designing the tool to help find those children who might otherwise get missed. In the most extreme cases, these might be children such as Victoria Climbié and Baby P, who were tortured and murdered by their guardians. The serious case reviews highlighted multiple missed opportunities for action, often because systems were not joined up, meaning no one was able to see full picture.
“If we're able to look at all of the data across the system relating to a child, then it might well be possible to bring that together and say, actually there's enough information here that we can do something about it.”
From womb to world
Across the world, fertility rates are falling, while families are choosing to have children later on in life. To help them conceive, many couples turn to assisted reproductive technologies such as IVF; however, success rates remain low and the process can be expensive. In the UK, treatment at a private clinic can cost more than £5,000 per cycle – in the US, it can be around $20,000 – and with no guarantee of success.
Mo Vali and Dr Staci Weiss hope that AI can change this. They are leading From Womb to World, one of ai@cam’s flagship AI-deas projects, which aims to improve prospective parents’ chances of having a baby by diagnosing fertility conditions early on and personalising fertility treatments.
“We're trying to democratise access to IVF outcomes and tackle a growing societal problem of declining fertility rates.”
Mo Vali
They are working with Professor Yau Thum at The Lister Fertility Clinic, one of the largest standalone private IVF clinics in the UK, to develop cheaper, less invasive and more accurate AI-assisted tests that can be used throughout the patient’s IVF journey. To do this, they are making use of the myriad different samples and datasets collected during the fertility process, from blood tests and ultrasound images to follicular fluid, as well as data encompassing demographic and cultural factors.
Building the AI tools was the easy bit, says Vali. The bigger challenge has been generating the datasets, clearing ethical and regulatory hurdles, and importantly, ensuring that sensitive data is properly anonymised and de-identified – vital for patient privacy and building public trust.
The team also hopes to use AI to improve, and make more accessible, 4D ultrasound scans that let the parents see their baby moving in the womb, capturing movements like thumb-sucking and yawning. This is important for strengthening the maternal bond during a potentially stressful time, says Weiss.
“Seeing their baby's face and watching it move creates a very different kind of physical, embodied reality and a bond between the mother and her child,” she says.
Consulting with women who have experienced first-hand the challenges of fertility treatments is providing valuable insights, while The Lister Fertility Clinic – a private clinic – is an ideal platform in which to test their ideas before providing tools for the wider public. It offers a smaller, more controlled environment where they can engage directly with senior clinicians.
“We want to ensure that the research that we are doing and the AI models that we're building work seamlessly before we go at scale,” says Vali.
Preventing cancer
Antonis Antoniou, Professor of Cancer Risk Prediction at Cambridge, has spent most of his career developing models that predict our risk of developing cancers. Now, AI promises to take his work to an entirely new level.
Antoniou has recently been announced as Director of the Cancer Data-Driven Discovery Programme, a £10million initiative that promises to transform how we detect, diagnose – and even prevent – cancer in the future. It’s a multi-institutional project, with partners across the UK, that will build infrastructure and create a multidisciplinary community of researchers, including training the next generation of researchers, with funding for 30 PhD places and early career research positions in cancer data sciences.
The programme will enable scientists to access and link a vast array of diverse health data sets, from GP clinics and cancer screening programmes to large cohort studies through to data generated through interactions with public services such as on occupation, educational attainment and other geospatial data on air pollution, housing quality and access to services. These will be used in combination with AI and state-of-the-art analytics.
“The funding will allow us to use these data sets to develop models that help us predict individual cancer risk and greatly improve our understanding of who is most at risk of developing cancer,” he says. “It will hopefully help us transform how we detect and prevent and diagnose cancer in the future.”
One of the key considerations of their work will be to ensure that the AI tools they develop do not inadvertently exacerbate inequalities.
“We have to be careful not to develop models that only work for people who are willing to participate in research studies or those who frequently interact with the healthcare sector, for example, and ensure we’re not ignoring those who can’t easily access healthcare services, perhaps because they live in areas of deprivation.”
Key to their programme has been the involvement of patients and members of the public, who, alongside clinical practitioners, have helped them from the outset to shape their programme.
“They were involved in co-developing our proposals from the planning phase, and going forward, they’ll continue to play a key role, helping guide how we work and to make sure that the data are used responsibly and safely,” he says.
The Cancer Data-Driven Detection programme is jointly supported by Cancer Research UK, the National Institute for Health & Care Research, the Engineering & Physical Sciences Research Council, Health Data Research UK, and Administrative Data Research UK.
Innovations in drug discovery
It’s just over 20 years since the first human genome was sequenced, opening up a new scientific field – genomics – and helping us understand how our bodies function. Since then, the number of so-called ‘omics’ – complete readouts of particular types of molecules in our bodies, such as proteins (proteomics) and metabolites (metabolomics) – has blossomed.
Dr Namshik Han from the Milner Therapeutics Institute is interested in how AI can mine this treasure trove to help discover new drugs.
“We’re applying AI approaches to dissect those really big data sets and try to identify meaningful, actionable drug targets,” he says.
His team works with partners who can take these targets to the next stage, such as by developing chemical compounds to act on these targets, testing them in cells and animals, and then taking them through clinical trials.
The Milner Institute acts as a bridge between academia and industry to accelerate this process, partnering with dozens of academic institutes, industry partners, biotech, pharma and venture capitalists. But at the ‘bleeding edge’ of Han’s work is his collaborations with tech companies.
Han is interested in how quantum computers, which use principles of quantum mechanics to enable much faster and more powerful calculations, can address problems such as the complex chemistry underpinning drug development.
“We’ve shown that quantum algorithms see things that conventional AI algorithms don’t.” Han says.
His lab has used quantum algorithm to explore massive networks comprising tens of thousands of human proteins. When conventional AI explores these networks, it only looks at certain areas, whereas Han showed that quantum algorithms cover the entire network.
AI has the potential to improve every aspect of drug discovery – from identifying targets, as Han is doing, to optimising clinical trials, potentially reducing the cost of new medications and ensuring patients benefit faster. But that’s not what really excites Han.
“Take cancer, for example,” he says. “There are many different types, and for some of them we don’t have specific drugs to treat them. Instead, we have to use a drug for a related cancer and give that to the patient, which is not ideal.
“Quantum based AI will open up a completely new door to find truly innovative drugs which we’ve never thought of before. That’s where the real impact has to be.”
Better AI makes for healthier living
Professors Andres Floto, Mihaela van der Schaar and Eoin McKinney on the power of AI to transform health and medicine. Read more
Published 7 April 2025
Images
- Artificial intelligence in healthcare (Just_Super/Getty Images)
- The doctor is delivering good news to the senior patient (Halfpoint Images/Getty Images)
- Medical check up (SolStock/Getty Images)
- Woman standing under pink flowers (Ángel López/Unsplash)
- Pregnant woman using telemedicine app on smartphone (Oscar Wong/Getty Images)
- Nurse assisting patient during mammogram (Tom Werner/Getty Images)
- Male medical worker pipetting chemical in test tube while working in laboratory (izusek/Getty Images)
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