A new British-French partnership is set to tackle women’s health challenges and infectious diseases using emerging tech such as AI and supercomputers.
As well as bringing together data from across the globe on a range of diseases such as E. coli, researchers from both nations will use novel imaging and AI technologies to study infections including TB, malaria, and emerging viruses, sharing research expertise, and working together on joint funding bids.
£900,000 of UK government funding has been committed to furthering the partnership between the Bristol Centre for Supercomputing, which hosts the Isambard‑AI supercomputer, and France’s computing centre GENCI. £300,000 UK funding and €330,000 from the French government has also been agreed to support early-career researchers and new collaboration opportunities.
“At the core of the research in women’s health, infectious diseases, data science and advanced bioimaging is the UK–France Strategic Biomedical Alliance in Health and AI, a partnership between the University of Oxford, Université Paris Cité, Institut Pasteur and the UK and French national advanced imaging facilities, the Diamond Light Source and Synchrotron SOLEIL,” the UK government shares. “Together, these partners combine world‑leading science with national bioimaging infrastructure and technology development.”
The UK’s science and technology secretary, Liz Kendall, highlighted the promise of the partnership in tackling “some of the biggest challenges in women’s health”, delivering safer and healthier pregnancies, and accelerating the global fight against infectious diseases. “We are determined to build on that spirit of co-operation with our G7 partners this week, to drive forward work on some of the most important issues that affect us all, from AI adoption to keeping kids safe online,” Kendall added.
Philippe Guérin, director of the Infectious Diseases Data Observatory at the University of Oxford, said: “Through this Alliance we will be able to see disease in new ways – combining the technology of two of the world’s most powerful synchrotrons, with the world’s greatest research minds, to understand how infections develop, spread and respond to treatment at an unprecedented level of detail. That deeper understanding will drive innovation in diagnostics and therapies, helping improve outcomes for patients and at the same time strengthening preparedness for future health threats worldwide.”
A collaboration will also be taking place between Imperial College London and the French National Center for Scientific Research, looking to advance research into metabolism and health challenges such as heart disease, cancer, and neurodegenerative disorders.
Wider trend: Health research
A research collaboration involving Moorfields Eye Hospital NHS Foundation Trust, the UCL Institute of Ophthalmology, and Lufthansa, has warned of the “automation paradox” involving the deskilling of clinicians with the introduction of AI and automation. Five recommendations are put forward: monitoring real-world clinician performance without AI assistance and implementing minimum unaided practice requirements; prioritising independent reasoning skills prior to the introduction of automation; ensuring clinicians understand AI limitations; introducing mandatory simulation training on AI failures; and cultivating “operational understanding” of how AI tools make decisions and when to override them.
A study funded by the National Institutes of Health has developed an AI tool offering clinical decision support to clinicians by predicting patients at risk of intimate partner violence (IPV) from data collected during medical visits. Led by researchers from Harvard Medical School, the study trained a machine learning model using several years of hospital data from around 850 female patients affected by IPV, along with 5,200 control patients matched on age and demographics. Whilst not intended for making “definitive diagnoses”, researchers suggest the tool could be used in a proactive approach to IPV intervention, improving long-term health outcomes for at-risk patients.
A US National Institutes of Health-supported study has developed an AI algorithm trained on EHR data to predict rare disease, with plans to scale over time to suggest when disease may appear, and how patients will respond to treatment. The WEakly Supervised Transformer (WEST) algorithm is reportedly capable of using “noisy”, incomplete, inaccurate, or non-informative data from EHRs to predict whether a patient is likely to have a specific rare condition. “In particular, the model can learn from patients both with and without confirmed diagnoses, using less precise outcome information to identify diagnostic patterns,” NIH states. “This is particularly important when studying rare diseases, where knowledge may be limited and the high-quality labeled data typically needed for model training are often unavailable.”
