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.
Ten outstanding Cambridge researchers have been elected as Fellows of the Royal Society, the UK’s national academy of sciences and the oldest science academy in continuous existence.
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.
Scientists have been able to track how a multi-drug resistant organism is able to evolve and spread widely among cystic fibrosis patients – showing that it can evolve rapidly within an individual during chronic infection.
World-leading AI technology developed by the Cambridge Centre for AI in Medicine and their colleagues – some of which is being showcased this week at the North American Cystic Fibrosis Conference 2020 – offers a glimpse of the future of precision medicine, and unprecedented predictive power to clinicians caring for individuals with the life-limiting condition.
The COVID-19 pandemic is the greatest global healthcare crisis of our generation, presenting enormous challenges to medical research, including clinical trials. Advances in machine learning are providing an opportunity to adapt clinical trials and lay the groundwork for smarter, faster and more flexible clinical trials in the future.
A University of Cambridge team led by Professor Mihaela van der Schaar and intensive care consultant Dr Ari Ercole of the Cambridge Centre for AI in Medicine (CCAIM) is calling for urgent research into the striking differences in COVID-19 deaths they have discovered between the intensive care units of NHS trusts across England.
Following from last week's call for governments to use machine learning and AI techniques to help in the fight against the COVID-19 pandemic, Professor Mihaela van der Schaar gives an update on a working proof of concept she has built using anonymised data from Public Health England.
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