̽��ֱ�� of Cambridge - medicine

Opinion: AI can transform health and medicine

cjb250 — Mon, 07 Apr 2025 08:00:37 +0000

AI has the potential to transform health and medicine. It won't be straightforward, but if we get it right, the benefits could be enormous. Andres Floto, Mihaela van der Schaar and Eoin McKinney explain.

Weasel testicles, stargazing and royal remedies: medieval medicine examined in Curious Cures exhibition

sjr81 — Thu, 27 Mar 2025 17:02:22 +0000

Ancient manuscripts reveal complex theories behind the terrifying treatments of the medieval era

Professor Duncan Richards appointed as Head of Department of Medicine

Anonymous — Fri, 06 Dec 2024 16:59:07 +0000

Professor Richards joins Cambridge from the ̽��ֱ�� of Oxford, where he has been since 2019. His particular research interest is the demonstration of clinical proof of concept of novel therapeutics through the application of experimental medicine techniques, especially human challenge studies.

As Climax Professor of Clinical Therapeutics, director of the Oxford Clinical Trial Research Unit (OCTRU), and the NIHR Oxford Clinical Research Facility, he led a broad portfolio focused on new medicines for multiple conditions. His focus has been the acceleration of promising new drug treatments through better decision-making in early phase clinical trials.

Professor Richards also brings with him a wealth of experience in a number of Pharmaceutical R&D clinical development roles. In 2003 he joined GSK and held a number of roles of increasing responsibility, latterly as Head of Clinical Pharmacology and Experimental Medicine, including directorship of GSK’s phase 1 and experimental medicine unit in Cambridge (CUC).

Commenting on his appointment, Professor Richards said: “As a clinical pharmacologist, I have been fortunate to work across a broad range of therapeutic areas over the years. I am excited by the breadth and depth of expertise within the Department of Medicine and look forward to working with the first-class scientific team. My goal is to work with the Department team, the Clinical School, and hospitals to maximise the impact of the important work taking place in Cambridge.”

Members of the department’s leadership team are looking forward to the continued development of the department under Professor Richards, building on its legacy of collaboration and groundbreaking translational research to drive our future success.

Professor Mark Wills, Interim Head of Department of Medicine, said: “Duncan brings to his new role a fantastic breadth of experience, which encompasses his clinical speciality in pharmacology, extensive experience of working within the pharmaceutical industry R&D at senior levels and most recently establishing academic clinical trials units and human challenge research facilities.

“I am very excited to welcome Duncan to the Department and looking forward to working with him, as he takes on the role of delivering of the Department of Medicine’s vision to increase the efficacy of translation of its world class fundamental research, and its impact upon clinical practice and patient wellbeing.”

Menna Clatworthy, Professor of Translational Immunology and Director of the Cambridge Institute for Therapeutic Immunology and Infectious Disease (CITIID), said: "Duncan has a wealth of leadership experience in biomedicine, in both academia and pharma. That skillset will be invaluable in ensuring the Department of Medicine continues to deliver world-leading research to transform patient outcomes."

Charlotte Summers, Professor of Intensive Care Medicine and Director of the Victor Phillip Dahdaleh Heart & Lung Research Institute, said: “Duncan’s exemplary track record of translating fundamental scientific discoveries into therapies that benefit patients will help us further increase the impact of our research as we continue our mission to improve human health.”

̽��ֱ��appointment underpins the recently announced five-year collaboration between GSK and the ̽��ֱ�� of Cambridge, the Cambridge-GSK Translational Immunology Collaboration (CG-TIC). ̽��ֱ��£50 million investment will accelerate research and development in kidney and respiratory diseases to improve patient outcomes.

Professor Richards will assume the role in February 2025, replacing Interim Head of Department Dr Mark Wills who was appointed after the departure of Professor Ken Smith in January 2024. Dr Wills will continue as Director of Research and Deputy Head of the Department of Medicine as well as leading his research group.

Professor Richards trained in medicine at Oxford ̽��ֱ�� and after junior doctor roles in London, he returned to Oxford as Clinical Lecturer in Clinical Pharmacology. His DM thesis research was on a translational model using platelet ion flux to interrogate angiotensin biology and he is author of the Oxford Handbook of Practical Drug Therapy and the 3rd edition of Drug Discovery and Development.

Professor Richards has been a core member of the UK COVID-19 Therapeutics Advisory Panel. He is a member of the Oxford Bioescalator Management Board, UK Prix Galien Prize Committee, and the therapeutic advisory committee of several national platform clinical trials.

Professor Duncan Richards has today been announced as the new Head of the Department of Medicine at the ̽��ֱ�� of Cambridge.

I am excited by the breadth and depth of expertise within the Department of Medicine and look forward to working with the first-class scientific team

Duncan Richards

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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Supertroopers: CAR-T cell cancer therapy

lw355 — Wed, 16 Oct 2024 08:00:15 +0000

A life-saving cancer therapy is being scaled up in Cambridge to deliver more treatments to more patients for more cancers.

Training AI models to answer ‘what if?’ questions could improve medical treatments

sc604 — Fri, 19 Apr 2024 08:02:54 +0000

Artificial intelligence techniques can be helpful for multiple medical applications, such as radiology or oncology, where the ability to recognise patterns in large volumes of data is vital. For these types of applications, the AI compares information against learned examples, draws conclusions, and makes extrapolations.

Now, an international team led by researchers from Ludwig-Maximilians-Universität München (LMU) and including researchers from the ̽��ֱ�� of Cambridge, is exploring the potential of a comparatively new branch of AI for diagnostics and therapy.

̽��ֱ��researchers found that causal machine learning (ML) can estimate treatment outcomes – and do so better than the machine learning methods generally used to date. Causal machine learning makes it easier for clinicians to personalise treatment strategies, which individually improves the health of patients.

̽��ֱ��results, reported in the journal Nature Medicine, suggest how causal machine learning could improve the effectiveness and safety of a variety of medical treatments.

Classical machine learning recognises patterns and discovers correlations. However, the principle of cause and effect remains closed to machines as a rule; they cannot address the question of why. When making therapy decisions for a patient, the ‘why’ is vital to achieve the best outcomes.

“Developing machine learning tools to address why and what if questions is empowering for clinicians, because it can strengthen their decision-making processes,” said senior author Professor Mihaela van der Schaar, Director of the Cambridge Centre for AI in Medicine. “But this sort of machine learning is far more complex than assessing personalised risk.”

For example, when attempting to determine therapy decisions for someone at risk of developing diabetes, classical ML would aim to predict how probable it is for a given patient with a range of risk factors to develop the disease. With causal ML, it would be possible to answer how the risk changes if the patient receives an anti-diabetes drug; that is, gauge the effect of a cause. It would also be possible to estimate whether metformin, the commonly-prescribed medication, would be the best treatment, or whether another treatment plan would be better.

To be able to estimate the effect of a hypothetical treatment, the AI models must learn to answer ‘what if?’ questions. “We give the machine rules for recognising the causal structure and correctly formalising the problem,” said Professor Stefan Feuerriegel from LMU, who led the research. “Then the machine has to learn to recognise the effects of interventions and understand, so to speak, how real-life consequences are mirrored in the data that has been fed into the computers.”

Even in situations for which reliable treatment standards do not yet exist or where randomised studies are not possible for ethical reasons because they always contain a placebo group, machines could still gauge potential treatment outcomes from the available patient data and form hypotheses for possible treatment plans, so the researchers hope.

With such real-world data, it should generally be possible to describe the patient cohorts with ever greater precision in the estimates, bringing individualised therapy decisions that much closer. Naturally, there would still be the challenge of ensuring the reliability and robustness of the methods.

“ ̽��ֱ��software we need for causal ML methods in medicine doesn’t exist out of the box,” says Feuerriegel. “Rather, complex modelling of the respective problem is required, involving close collaboration between AI experts and doctors.”

In other fields, such as marketing, explains Feuerriegel, the work with causal ML has already been in the testing phase for some years now. “Our goal is to bring the methods a step closer to practice,” he said. ̽��ֱ��paper describes the direction in which things could move over the coming years.”

“I have worked in this area for almost 10 years, working relentlessly in our lab with generations of students to crack this problem,” said van der Schaar, who is affiliated with the Departments of Applied Mathematics and Theoretical Physics, Engineering and Medicine. “It’s an extremely challenging area of machine learning, and seeing it come closer to clinical use, where it will empower clinicians and patients alike, is very satisfying.”

Van der Schaar is continuing to work closely with clinicians to validate these tools in diverse clinical settings, including transplantation, cancer and cardiovascular disease.

Reference:
Stefan Feuerriegel et al. ‘Causal machine learning for predicting treatments.’ Nature Medicine (2024). DOI: 10.1038/s41591-024-02902-1

Adapted from an LMU media release.

Machines can learn not only to make predictions, but to handle causal relationships. An international research team shows how this could make medical treatments safer, more efficient, and more personalised.

Yuichiro Chino via Getty Images

Computer-generated image of human brain

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Having a ‘regular doctor’ can significantly reduce GP workload, study finds

sc604 — Fri, 23 Feb 2024 01:11:40 +0000

In one of the largest studies of its kind, researchers from the ̽��ֱ�� of Cambridge and INSEAD analysed data from more than 10 million consultations in 381 English primary care practices over a period of 11 years.

̽��ֱ��results, reported in the journal Management Science, suggest that a long-term relationship between a patient and their doctor could both improve patient health and reduce workload for GPs.

̽��ֱ��researchers found that when patients were able to see their regular doctor for a consultation – a model known as continuity of care – they waited on average 18% longer between visits, compared to patients who saw a different doctor. ̽��ֱ��productivity benefit of continuity of care was larger for older patients, those with multiple chronic conditions, and individuals with mental health conditions.

Although it will not always be possible for a patient to see their regular GP, this productivity differential would translate to an estimated 5% reduction in consultations if all practices in England were providing the level of care continuity of the best 10% of practices.

Primary care in the UK is under enormous strain: patients struggle to get appointments, GPs are retiring early, and financial pressures are causing some practices to close. According to the Health Foundation and the Nuffield Trust, there is a significant shortfall of GPs in England, with a projected 15% increase required in the workforce. ̽��ֱ��problem is not limited to UK, however: the Association of American Medical Colleges estimates a shortfall of between 21,400 and 55,200 primary care physicians in the US by 2033.

“Productivity is a huge problem across all the whole of the UK – we wanted to see how that’s been playing out in GP practices,” said Dr Harshita Kajaria-Montag, the study’s lead author, who is now based at the Kelley School of Business at Indiana ̽��ֱ��. “Does the rapid access model make GPs more productive?”

“You can measure the productivity of GP surgeries in two ways: how many patients can you see in a day, or how much health can you provide in a day for those patients,” said co-author Professor Stefan Scholtes from Cambridge Judge Business School. “Some GP surgeries are industrialised in their approach: each patient will get seven or ten minutes before the GP has to move on to the next one.”

At English GP practices, roughly half of all appointments are with a patient’s regular doctor, but this number has been steadily declining over the past decade as GP practices come under increasing strain.

̽��ֱ��researchers used an anonymised dataset from the UK Clinical Practice Research Datalink, consisting of more than 10 million GP visits between 1 January 2007 and 31 December 2017. Using statistical models to account for confounding and selection bias, and restricting the sample to consultations with patients who had at least three consultations over the past two years, the researchers found that the time to a patient’s next visit is substantially longer when the patient sees the doctor they have seen most frequently over the past two years, while there is no operationally meaningful difference in consultation duration.

“ ̽��ֱ��impact is substantial: it could be the equivalent of increasing the GP workforce by five percent, which would significantly benefit both patients and the NHS,” said Scholtes. “Better health translates into less demand for future consultations. Prioritising continuity of care is crucial in enhancing productivity.”

“ ̽��ֱ��benefits of continuity of care are obvious from a relationship point of view,” said Kajaria-Montag. “If you’re a patient with complex health needs, you don’t want to have to explain your whole health history at every appointment. If you have a regular doctor who’s familiar with your history, it’s a far more efficient use of time, for doctor and patient.”

“A regular doctor may have a larger incentive to take more time to treat her regular patients thoroughly than a transactional provider,” said Scholtes. “Getting it right the first time will reduce her future workload by preventing revisits, which would likely be her responsibility, while a transactional provider is less likely to see the patient for her next visit.”

̽��ֱ��researchers emphasise that continuity of care does not only have the known benefits of better patient outcomes, better patient and GP experience, and reduced secondary care use, but also provides a surprisingly large productivity benefit for the GP practices themselves.

Reference:
Harshita Kajaria-Montag, Michael Freeman, Stefan Scholtes. ‘Continuity of Care Increases Physician Productivity in Primary Care.’ Management Science (2024). DOI: 10.1287/mnsc.2021.02015

If all GP practices moved to a model where patients saw the same doctor at each visit, it could significantly reduce doctor workload while improving patient health, a study suggests.

̽��ֱ��Good Brigade via Getty Images

Doctor examining a patient

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A habitable planet for healthy humans

plc32 — Wed, 13 Dec 2023 17:28:42 +0000

Cambridge Zero symposium gathers researchers to examine the connections between planetary and public health.

A very healthy relationship: the ̽��ֱ�� and the NHS

cjb250 — Mon, 03 Jul 2023 15:40:21 +0000

As the NHS celebrates its 75th anniversary, we look at how the close relationship between the ̽��ֱ�� and the hospitals on its doorstep is driving major improvements in how we care for patients.