A promising breakthrough involving the use of Artificial Intelligence (AI) to detect breast cancer was revealed at the start of the year 2020. Researchers from Google Health, DeepMind, Imperial College, London, the NHS, and the US Northwestern University developed an AI model that, like expert radiologists, was able to correctly classify cancer from X-ray images with precision. The AI model trained by analysing photographs of nearly 29,000 women has the ability to revolutionise healthcare, as it can not only decrease the risk of mistakes, but also reduce the burden on health systems.
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. For instance, OpenAI, an AI research and development company, estimated in 2018 that since 2012, the amount of computational power needed to train the largest AI models had doubled every 3.4 months. Researchers at the Massachusetts Institute of Technology recently warned, looking at the increased demand for computational power for AI, that deep learning is approaching computational limits.
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. AI requires advanced hardware that allows trillions of calculations per second to be processed. This is where it fits AI best.
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