From accelerated diagnosis and faster delivery of life-saving treatments to proactive population health prediction and management, artificial intelligence (AI) and machine learning (ML) have the potential to transform how care is delivered.
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.
Learn moreThere are many AI/ML use cases being considered, trialled and rolled out around the world, but these ones in particular require high-performance computing (HPC) to be moved from the data centre to the point-of-care
Iceotope enables AI/ML-powered diagnostic imaging to capture abnormalities, predict diseases, revaluate treatment and plan surgery
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Iceotope enables AI/ML-powered structured data, actionable insights, longitudinal understanding and precision diagnosis
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Iceotope enables AI/ML-powered understanding, integration and examination of large data sets
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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.
View the solutionHealthcare 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.
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.
