Blog

September 30, 2022 / by Challenger Mishra and Subhayan Roy Moulik / In

Quantum computing and machine learning for science

The innate ability of quantum computers to simulate quantum mechanics opens up new paradigms for computation that were largely unimagined before, and could pave the way for exciting new breakthroughs. Searching for problems that are intractable for a classical computer, but which a quantum device can solve, is an active area of research. Although such a problem need not necessarily have a scientific rendition, utilising quantum computing to achieve breakthroughs in scientific problems could pave a way for demonstrating quantum supremacy itself.

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May 17, 2022 / by Dr Romit Samanta, NIHR Clinical Lecturer in Intensive Care Medicine, University of Cambridge / In

How can we...use AI to understand acute respiratory distress syndrome?

Acute respiratory distress syndrome (ARDS) affects one in 10 critical care patients. It is a condition in which the lungs cannot provide the body’s vital organs with enough oxygen, often as a result of infection. It is fairly common, but under-recognised and difficult to treat, partly due a lack of understanding of the underlying biology. Could machine learning help us identify more effective treatments for patients with ARDS?

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March 23, 2022 / by Daniel Esteban-Ferrer, CEO, VRi / In Accelerate-Spark data science residency

How can we…use AI to take bio-imaging to the next dimension?

Huge amounts of data are generated by modern microscopy and imaging studies. Our work helps researchers make sense of this data by visualising datasets in intuitive and immersive formats. This has applications across a range of domains, and our current work is investigating how doctors can make use of new software to analyse brain scans and prepare for surgeries.

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February 01, 2022 / by Diana Robinson, University of Cambridge / In Accelerate-Spark data science residency

How can we... Use AI to enable doctors to build their own models with clinical data?

Having the full institutional history of patient data to draw upon in decision making is one advantage of AI that clinicians are keen to embrace. AI-enabled data analysis could help clinicians pursue more effective treatments for issues such as post-operative bleeding, but AI tools will need to be fit for clinical practice.

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