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How to overcome the complexity and cost
AI in healthcare- Artificial intelligence (AI) promises to revolutionize healthcare. The underlying combination of Machine Learning and analytics can process medical data sets so large and medical images so numerous that they are beyond the scale of researchers, physicians and staff. In so doing, this AI duo promises to help identify patients at risk and prevent the onset of diseases and medical conditions. For existing patients, the hope is AI can identify hidden illnesses, pinpoint medical problems and in the development and application of treatments that assist patient recovery.
Yet adoption has been held back thanks to the cost and complexity of building and owning the kinds of high-performance systems needed. That is changing, though, as processors become optimised for AI training and inference and as they are combined with the more powerful software.
As an example of the former, the second generation Intel® Xeon® Scalable processors are a case in point. They deliver anything up to a 30 times increase in performance for AI inference compared to the previous generation of Xeon®. Intel® Deep Learning Boost, meanwhile, includes specific x86 extensions that help accelerate convolutional neural-network-based algorithms. Performance is further improved for both batch and real-time inference using the vector-neural network instruction (VNNI) function that reduces the number and the complexity of convolution operations required for AI inference. VNNI also cuts down the volume of compute power and memory access required, further reducing latency and increasing performance of AI applications.
Running production-grade AI at scale goes beyond just hardware – it requires powerful software, too. Here, the industry seems to be coalescing around Google’s open-source TensorFlow for Machine Learning – a framework for building and training the kinds of large-scale numerical computation demanded by AI. TensorFlow uses Python to deliver a front-end API for building applications but executes in C++. It is well suited to training and running neural networks for the image recognition workloads demanded in common medical environments such as radiology and CT scans.