Enabling distributed AI/ML workloads at the point-of-care

Introducing Ku:l Micro DC and Ku:l Server

Our fully-integrated Ku:l Micro Data Centre coming in multiple deployment configurations and our passively-cooled Ku:l Server offering server-class compute in any orientation can enable distributed AI/ML workloads in virtually any location and at any scale to ensure real-time value at the point-of-care.

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The challenges of deploying AI/ML workloads in healthcare

The value AI and ML create in giving decision makers the ability to take meaningful action based on data insights at the point-of-care is clear to see. What’s not so clear is the long list of challenges facing the deployment of the necessary computing power required to run these AI/HPC workloads.

Deploying IT at the point-of-care means deploying it in close proximity to people. Whether it be bed side or under a desk, people present risk to the IT in terms of accidental damage, tampering and contamination whilst the IT presents risk to the people in terms of noise, heat, germs and space consumption.

Power availability, power draw and its sustainability all have to be considered as does the impact of latency, resilience and scalability on real-time decision making at the point-of-care.

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The growing demand for healthcare services

Healthcare is one of the major success stories of our times with advances in medicine, supported by progress in public health, research, innovation and technology, significantly reducing mortality and morbidity.

As longevity increases, healthcare systems face growing demand for their services, rising costs and a workforce that is struggling to meet the needs of its patients. Demand is driven by a combination of unstoppable forces such as population ageing, changing patient expectations, a shift in lifestyle choices, and the never-ending cycle of innovation. Without major structural and transformational change, healthcare systems will struggle to remain sustainable.

The AI-powered future is already here

By 2030, AI will access multiple sources of data to reveal patterns in disease and aid treatment and care. By then healthcare systems will be able to predict an individual’s risk of certain diseases and suggest preventative measures. Today, we’re already seeing AI-powered solutions addressing routine, repetitive and largely administrative tasks on a daily basis.

There is also a growing number of software vendors competing in the Healthcare space with AI applications based on imaging being used in specialties such as radiology, pathology and ophthalmology.

Since the outbreak of Covid-19 we’ve seen significant investment around the world in AI-epowered next-generation genomic sequencing which is accelerating precision medicine with the ultimate goal of shifting clinical treatment from a “trial-and-error” approach to “the right drug, for the right patient, at the right time” approach. AI has already started to personalise healthcare.


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