London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare gets the support of Run:AI, as a technology provider. The company known for AI infrastructure virtualization would enable it to manage and utilize resources more efficiently, and speed up completion time for training data science models rendering better visibility, control and more elastic allocation. 

CEO and co-founder of Run:AI, Omri Geller mentioned that especially now with the COVID-19 pandemic, the use of advanced AI in healthcare is even more prominent and can actually save lives. He said “We’re proud to be working with the London AI Centre to help ensure their important research can get the best use out of their hardware, so they can run more experiments quickly and efficiently.”

After the installation of Run:AI, the centre has considerably reduced the time taken to complete its experiments. Looking at some stats after and before Run:AI implementation shows a drastic improvement. Over a 40-day period researchers were able to run 3000 experiments compared to 162 in a simulation of before implementation. The average turnaround time reduced to being a day and a half for experiments where it used to hover around 46 days without Run:AI incorporated in the centre’s infrastructure.

“Our experiments can take days or minutes, using a trickle of computing power or a whole cluster,” said Dr. M. Jorge Cardoso, CTO of the AI Centre and Associate Professor & Senior Lecturer in AI at King’s College London.

The AI Centre has been set up by the UK Government’s Industrial Strategy Challenge Fund, along with four NHS trusts, Queen Mary University London, Imperial College London, and a number of industry partners. Its recent contribution of an AI Diagnostic tool that discovered anosmia (losing sense and taste smell) a stronger and reliable indicator of COVID-19 infection than fever, prompting the UK Government to amend its official advice on suspected infections.

Based in St Thomas’ Hospital and led by King’s College London, the AI centre in order to train sophisticated AI learning algorithms to create new tools for more effective screening, personalized therapies and faster diagnosis, makes use of large amount of de-identified data, medical images, and patient clinical pathway data held in the NHS repository.

According to Cardoso, with Run:AI there’s been a marked improvement in experimentation speed and GPU hardware utilization, a reduction in time taken for results allows them to focus on seeking answers to more critical questions about people’s health and lives.

About Run:AI

Run:AI credited for the world’s first orchestration and virtualization platform for AI infrastructure, was founded in 2018, with the goal of solving challenges faced by IT leaders, MLOps, and data science teams because of their limited ability to allocate and control expensive compute resources to achieve optimal speed and utilization. The platform built on top of Kubernetes enables simple integration with existing IT and data science workflows. IT teams can retain centralized, cross-site control and real-time visibility over resource queuing, provisioning, and utilization, either on premises or in the cloud and Data Scientists can seamlessly consume massive amounts of GPU power to accelerate and improve their research.

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