Why HPC and AI convergence is changing the healthcare industry

A promising breakthrough involving the use of Artificial Intelligence (AI) to detect breast cancer was revealed at the start of the year 2020. An AI model was developed by researchers from Google Health, DeepMind, Imperial College, London, the NHS, and Northwestern University in the US, who were able to correctly classify cancer from X-ray images with an accuracy like expert radiologists

The greatest challenge in healthcare today, as we can see, while the potential is immense, is the fact that solving complex problems requires large quantities of data and intense computing capacity to analyse this knowledge. In 2018, for instance, OpenAI, an AI research and development company, reported that every 3.4 months the amount of computing power needed to train the largest AI models had doubled.

The combination of AI and High-Performance Computing (HPC) is extremely advantageous in this context, as it can lead to a win-win situation for any healthcare ecosystem stakeholder. The relationship between HPC and AI is symbiotic and can complement each other. To achieve high-performance targets, HPC utilises a cluster of systems working together as a cohesive unit.

To meet unforeseen demand, the costs of most conventional HPC systems are disproportionate. Healthcare organisations should start using High-Performance Computing-as-a-Service (HPCaaS) to prevent these problems, which offers guaranteed performance cost efficiencies and requires zero CAPEX investment and can be consumed using a pay-as-you-use model. Even, scalability is not a concern and can be scaled up or down.
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With the ability to run large number of processes in parallel, GPUs in HPC systems can process large data sets in less amount of time. From an AI perspective, this also allows organisations to process significantly higher volumes of data, which helps improve the AI model.